The Kinesiology laboratory at UNIGE/HUG conducts movement assessments for clinical and research purposes. This project aims to improve the diagnosis of Normal Pressure Hydrocephalus (NPH) by implementing and evaluating motor performance in patients’ home environments. The study will focus on tracking gait parameters, particularly walking speed, over several days before and after a lumbar puncture. By utilizing wearable motion sensors, the research seeks to objectively measure symptom changes and enhance diagnostic accuracy for NPH patients. The project will involve analyzing various gait variables, assessing their psychometric properties, and evaluating the clinical feasibility of this novel diagnostic protocol at the Geneva University Hospitals (HUG).

Required Skills and Background

• Background in biomedical engineering or neuroscience
• Experience with data analysis and signal processing
• Familiarity with wearable sensor technology
• Basic knowledge of neurological disorders, particularly NPH
• Experience with statistical tools (e.g., JASP, R) is beneficial but not mandatory
• Openness to work with clinicians, caregivers, and an interdisciplinary team 

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The Kinesiology laboratory at UNIGE/HUG conducts movement assessments for clinical and research purposes. Specifically, it evaluates patients undergoing reverse total shoulder arthroplasty, typically after massive rotator cuff tears. This assessment is done before surgery and one year post-surgery, after rehabilitation. It includes shoulder kinematics and muscle activity of 7 shoulder muscles during various tasks.

A study of muscle synergies, how muscles coordinate to create efficient movement, can reveal adaptations in the case of a cuff rupture and the prosthesis’s impact on joint function. The project aims to analyse and compare pre- and post-operative muscle synergies using HUG data, with asymptomatic participants as a reference. The research will address: 1) How does a cuff tear affect shoulder muscle synergies? 2) How does reverse total shoulder arthroplasty impact these synergies?

Required Skills and Background

• Background in biomedical engineering or neuroscience
• Matlab and/or Python programming basis (mandatory)
• Knowledge of data analysis (mandatory)
• Knowledge of statistics and electromyography (optional)
• Languages: English (mandatory), French (optional)

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This project, part of the NCCR Evolving Language – WP Learning Trajectories, develops scalable digital intervention tools to support speech therapists in enhancing literacy acquisition. Building on methodologies such as that described in Pasqualotto et al. Nature Human Behavior, we target cognitive control processes such as working memory, attention, and inhibition as well as reading-related activities that include language-specific training tasks focused on phoneme discrimination, phonological memory, and decoding.

The master student’s tasks will include the following tasks:

Linguistic Material Development: Assist in creating and adapting linguistic training materials in multiple languages, including French, Italian, German, and/or English.

Data Collection Coordination: Organize and oversee data collection efforts in schools across Switzerland and neighboring countries (France, Italy, or Germany) using the developed intervention tools.

User Guidance and Support: Provide training and guidance (primarily in French) for therapists on how to use the intervention tools effectively with patients and their families.

Data Analysis and Organization: Analyze the collected data using statistical tools and ensure its proper organization for reporting and further use.

Open Science Practices: Contribute to open science practices by preparing datasets, documentation, and code for public sharing on repositories such as OSF or GitHub, ensuring transparency and reproducibility.

Support the research team in drafting pre-registrations for planned analyses and preparing manuscripts for open-access publication.

Required Skills

• Background in neuroscience, psychology, education, or statistics.
• Interest in digital interventions and literacy acquisition.
• Statistical analysis skills and familiarity with tools like R, Python, or SPSS.
• Familiarity with cognitive or reading development assessment tools is a plus.
• Proficiency in Italian, German, or French is needed.

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Vulnerability to distraction varies across the general population and significantly affects one’s capacity to stay focused on and successfully complete the task at hand, whether at school, on the road, or at work. This project consists in assessing the reliability of a novel ERP marker of distractibility in children and adults.

During this project, students will be trained in EEG data acquisition and pre-processing in various populations including children. Previous knowledge in EEG or other brain imaging techniques a plus, some familiarity with coding and statistics also a plus.

Required Skills

• Background in neuroscience, psychology, computer science or statistics.
• Interest in attention and executive functions.
• Statistical analysis skills and familiarity with tools like R, Python, or SPSS.
• Familiarity with brain imaging and specifically EEG a plus.
• Ability to function within the lab in English ; French or German a plus

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This project aims to investigate the dynamic functional connectivity between the cerebellum and the hippocampus in patients with schizophrenia. The student will understand and explore cerebellum-hippocampus dynamic functional connectivity, and how it relates to the distinct symptoms of schizophrenia (negative, positive and cognitive) in a longitudinal cohort working closely with experts in dynamic functional connectivity methods. Additionally, the student will assist in data acquisition for a randomized controlled trial, which includes neuroimaging data, clinical assessment, and transcranial magnetic stimulation (TMS), a non-invasive neurostimulation technique that has shown promise in improving symptoms of neuropsychiatric disorders.

Required Skills

Preferred background in clinical fields (e.g., psychology, medicine or equivalent). Applicants from other disciplines with relevant skills and interests are also welcome. Strong interest in clinical neurosciences and psychiatry, particularly schizophrenia Experience with statistics and programming language (MATLAB, Python, or R), or willingness to learn Ideally, prior experience with clinical work and data acquisition in human populations Proficiency in French is required, especially for data acquisition Familiarity with neuroimaging techniques is a plus Proactive attitude

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Transcranial Magnetic Stimulation (TMS) is a non-invasive neurostimulation technique that has shown promise in improving symptoms of neuropsychiatric disorders. Cortical dosing of TMS is an emerging field with immense potential for personalization of non-invasive stimulation protocols. In the proposed project, we aim to optimize TMS stimulation protocols by investigating different approaches to personalized electrical field (E-field) dosing computation for the cerebellum.
The student will work closely with neuroimaging and computational scientists to apply computational methods for electrical field dosing using brain models derived from MRI scans in schizophrenia. He/she will adopt a proactive approach to conducting thorough literature reviews, aiming to comprehend current research and identify gaps in the field. He/she will use statistical techniques for data analysis for brain circuit therapy in schizophrenia.

Required Skills

• Preferred background: Mathematics, Computer Science, Medicine, Psychology
• Good programing skills in Python and MATLAB or willingness to learn
• Interest in neuropsychiatric disorders, particularly schizophrenia
• Good understanding of brain anatomy and MRI data processing or willingness to learn
• Good knowledge of statistical methods for data analysis
• Proficiency in French is a plus
• Proactive attitude

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Neuroimaging and translational psychiatry (Indrit Bègue)

In neuroimaging, magnetic resonance imaging (MRI) acquisitions increasingly comprise diffusion MRI (dMRI), to infer the strength of physical wiring between brain regions, and functional MRI (fMRI), to track regional brain activity over time. Graph signal processing (GSP) is an emerging data science field, drawing on linear algebra and graph theory, with potential to jointly study brain structure and function.
This project seeks to develop novel clinically relevant GSP concepts by focusing on the cerebellum, a hallmark brain structure with deep implications for many brain disorders. The student will first gain familiarity with existing GSP notions, primarily devised to operate at the level of the cortex. Second, he/she will propose novel theoretical concepts to leverage GSP to cerebellar data. Third, he/she will benchmark them on a combined dMRI/fMRI dataset and apply them to contrast healthy controls and patients with schizophrenia in a separate dataset. Supervision will be provided by a GSP expert, also with input from clinical neuroscientists.

Required Skills

• Strong interest in mathematics (especially linear algebra and/or graph theory) and data science
• Curiosity towards clinical neurosciences and psychiatry, particularly schizophrenia
• Experience with statistics and programming languages (MATLAB, Python, or R)
• Familiarity with neuroimaging techniques is appreciated but not mandatory
• Proactivity, willingness to learn and “try again, fail again, fail better” attitude

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This project investigates the mechanisms underlying regression in Autism Spectrum Disorder (ASD), with a focus on Phelan-McDermid Syndrome (PMS), characterized by mutations or deletions of the SHANK3 gene. The study explores how disruptions in synaptic plasticity and neuroinflammation contribute to skill loss and behavioral deficits in animal model of ASD. Using advanced in vivo and in vitro methods, we aim to dissect the temporal progression of neuronal dysfunction in SHANK3-deficient mouse models, analyze circuit vulnerability, and identify potential therapeutic targets to mitigate regression.

Required Skills

• Neuroscience Background: basic knowledge of cell biology and neuronal function
• Basic Programming Knowledge: Familiarity with Python or MATLAB for data analysis and visualization.

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This project explores how native and non-native French speakers process word ambiguity with and without context using multimodal neuroimaging techniques. Exploiting a natural feature of language, colexification (i.e., when one word has multiple meanings, such as letter), we investigate the neural dynamics of ambiguous word processing and how it is modulated by sentence context in groups of subjects with different language backgrounds. Using magnetoencephalography (MEG), we track the spectrotemporal correlates of lexical semantic processing and control across time, and explore how language experience (e.g., proficiency) shapes these mechanisms in non-native speakers. Combining MEG data with MRI allows us to precisely localize temporally-resolved MEG activity using spatially-resolved structural brain imaging. Students will learn how to acquire MEG and MRI data using this experimental paradigm and develop hypotheses and analytical strategies using the MEG and/or MRI data (depending on their interests) alongside multilingual language experience measures

Required Skills

• Basic knowledge of MEG data acquisition and analysis
• Good experience with the Python programming language.
• Interest in cognitive and language neuroscience.
• Experience with statistical tools (e.g., R) is beneficial but not mandatory.
• Interest or experience with reproducible methods and workflows (e.g., OSF) is beneficial but not mandatory.
• Good working knowledge of French and English

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This project explores the organization and function of the brain’s language network, focusing on how it varies across individuals, languages, and tasks. Using fMRI, we’ll examine brain activity in participants as they engage in a range of activities, from traditional language tasks to watching movies. Data has already been collected within the Courtois Project on Neuronal Modelling (https://www.cneuromod.ca/). The deep phenotyping approach allows us to map the language network in detail and understand how it interacts with other brain systems involved in memory, emotion, and complex thought. Students will gain experience with fMRI data analysis, contributing to a deeper understanding of how the brain supports our unique human capacity for language.

Required Skills

• Basic knowledge of fMRI data acquisition and analysis
• Basic experience with the Python programming language.
• Interest in cognitive and language neuroscience.
• Interest or experience with reproducible methods and workflows (e.g., OSF) is beneficial but not mandatory.

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This project investigates how language and speech patterns can reveal early signs of neurodegenerative diseases like Alzheimer’s in multilingual individuals. We’re particularly interested in how these patterns differ across languages and between a person’s first and second languages. Using state-of-the-art speech analysis and brain imaging techniques like EEG and MRI, we aim to identify specific markers that could help diagnose these conditions earlier and more accurately. This research has the potential to improve healthcare access for diverse populations and advance our understanding of how neurodegeneration affects the brain. Students will gain valuable experience in collecting and analyzing EEG data from multilingual patients, contributing to the development of more effective diagnostic tools and treatments.

Required Skills

• Basic knowledge of EEG data acquisition and analysis
• Basic experience with the Python programming language.
• Interest in cognitive, clinical, and language neuroscience.
• Interest or experience with reproducible methods and workflows (e.g., OSF) is beneficial but not mandatory.
• Knowledge of Spanish and/or German is beneficial but not mandatory (any additional language spoken will be a plus).
• Good working knowledge of French and English.

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This project investigates the neural mechanisms underlying eye contact during social interactions using EEG hyperscanning and microstate analysis. EEG hyperscanning enables the simultaneous recording of brain activity from multiple individuals, allowing us to explore neural synchrony and brain coupling during dynamic exchanges. The inclusion of microstate analysis will provide a deeper understanding of the temporal organization of brain activity during sustained eye contact, comparing direct versus averted gaze and their influence on social coordination and empathy. Students will engage in both pilot and full-scale studies, progressively transitioning from learning EEG acquisition and preprocessing in their first year to designing and conducting experiments in their second year.

Required Skills

• Basic knowledge of EEG data acquisition and analysis
• Interest or experience in EEG microstate analysis is highly advantageous.
• Good experience with programming languages such as Python or MATLAB.
• Interest in social cognition and neuroscience.
• Experience with statistical tools (e.g., JASP, R) is beneficial but not mandatory.

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Affective computing is a specialized area of computer science dedicated to the recognition of user emotions through various affective cues, such as facial expressions, voice, and physiological reactions. Traditionally, the majority of studies in this field have focused on predicting self-reported emotions, whether discrete emotions (e.g. fear, anger, joy, sadness) or their representations in continuous spaces (e.g. valence and arousal). However, this approach can be costly and time-consuming due to the need for extensive annotations.In this project, we propose to utilize deep learning models with electroencephalographic (EEG) signals to predict electrodermal activity (EDA) as an alternative to self-reported emotions. This approach is based on the assumption that EDA can serve as a reliable measure of arousal, equivalent to or even surpassing self-reports.

The student will have to:

• establish a state-of-the-art and implement the most relevant models for self-reported arousal assessment;
• pre-process EDA signals (EDA signal decomposition and driving signal extraction);
• modify the state-of-the-art models so they can predict EDA;
• compare the performance of this method to self-reported arousal assessment, for instance by comparing its ability to generalize on unseen data;
• all algorithms and models will be employed on existing datasets (e.g. DEAP, MAHNOB, SEED).

Required Skills

• background in computer science, statistics or related fields
• signal processing
• deep machine learning
• good experience with Python
• interest toward human-computer interaction

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Flexible and effective behavior depends on our ability to distribute attention across sensory modalities. Cross-modal attention is particularly crucial within the peripersonal space, where objects within reach are perceived through multiple sensory modalities, especially vision and touch. Like humans, mice rely on visuo-tactile information to locate objects and discern their shape and movement. Neurons exhibiting visuo-tactile responses interact with the premotor cortex, which is known to play a role in decision-making. However, how the functional organization of multisensory areas is influenced by top-down attentional states remains unexplored. In this project, the Master student will investigate how the spatial organization of visuo-tactile responses provides crucial insights into the interaction of these sensory modalities. This study aims to uncover how saliency maps, which prioritize certain stimuli, are formed as mice dynamically shift their focus between sensory inputs.

Required Skills

Preferred background: Biology, Mathematics, Physics, Genetics, Computer Science, Medicine, Cognitive sciences  

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Uncovering the functional and structural brain changes that facilitate the learning of complex behaviors remains a central challenge in neuroscience. A key hurdle is mapping the spatiotemporal dynamics of synaptic plasticity across various scales and stages—from entire brain networks down to individual synapses—and correlating these dynamics with behavioral adaptations. This project aims to integrate experimental and computational tools to link an animal’s learning abilities with functional and structural synaptic changes. Advanced experimental methods will enable the Master student to measure synaptic changes across the entire brain, identifying the cortical areas critical for mice to learn and perform goal-directed sensorimotor tasks. This research has the potential to significantly enhance our understanding of the relationships between structure, function, and behavior in the context of learning.

Required Skills

Preferred background: Biology, Mathematics, Physics, Genetics, Computer Science, Medicine, Cognitive sciences  

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Decision-making is a fundamental process in neuroscience, orchestrating multiple systems in the brain. It offers a window into the cognitive realm, as an individual’s decisions are profoundly influenced by their current mental state and knowledge of the world. The lab has developed a computational model of decision-making that accurately reflects knowledge acquisition in mice. This model, which incorporates physiological phenomena such as state-dependent appetite motivation, deepens our understanding of learning and decision-making. In this project, the Master student will use this theoretical framework to test key predictions about mouse sensorimotor learning and decision-making. The student will fit behavioral data from individual mice to the model to uncover latent internal variables and intrinsic behavioral traits. Predictions derived from these fits will be tested in new tasks to evaluate the model’s accuracy in capturing mouse behavior across different scenarios. This research will provide essential data to validate the model, which has the potential to become an invaluable tool for the neuroscience community.

Required Skills

Preferred background: Biology, Mathematics, Physics, Genetics, Computer Science, Medicine, Cognitive sciences  

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The segmentation of the claustrum in neuroimaging holds promise for advancing our understanding of its structure-function relationships. Accurate segmentation is not only essential for elucidating its connectivity patterns but also for uncovering its potential role in neurological and psychiatric conditions. You will be working with a multidisciplinary team of clinicians and engineers as part of a bigger project that aims at segmentation and functional characterization of the claustrum. We aim to leverage a rich developmental dataset of individuals with 22q11.2 deletion syndrome (22q11DS), a genetic condition associated with high rates of psychopathology. This dataset includes both 3T and high-resolution 7T MRI scans, providing a unique opportunity to explore the structure and connectivity of the claustrum. For more details about the overall project click here: MasterThesisProject_Claustrum_DIPLab.pdf

Required Skills

• Basic knowledge of MRI data acquisition and analysis
• Basic knowledge of cell biology and neuronal function
• Familiarity with programming languages such as MATLAB or Python
• Interest in clinical neuroscience, psychopathologies, MRI and coding is highly advantageous.

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Dementia, characterized by severe cognitive impairments, poses a growing global burden, with Alzheimer’s disease (AD) as its primary cause. AD pathophysiology is mostly characterized by the accumulation of amyloid beta plaques in the brain, which is also the main hallmark of the deterministic amyloid cascade hypothesis. However, recent literature suggests that the amyloid cascade may not fully explain its variability in clinical practice, leading to the proposal of a probabilistic model. This model suggests that genetic and stochastic factors, like APOEe4, influence the amyloid cascade’s penetrance. This research project aims to test the probabilistic model, exploring genetic and stochastic factors in AD pathology and clinical expression using clinical, neuropsychological, and biomarker data from the Geneva Memory Center cohort.

Required Skills

Background in medicine, psychology, cognitive science, biomedical science, engineering, biology, or related field. Interest in dementia, Alzheimer’s disease, biomarkers and cognition. Familiarity with clinical research methodologies and statistical analysis (i.e. R) will be preferred.

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This project aims to advance the Brain Health Services (BHS) initiative by developing and validating a risk assessment protocol, personalized risk communication protocols and intervention strategies for the prevention of cognitive decline. Leveraging data from clinical cohorts and biomarkers, the research will focus on exploring the dementia risk factors in a clinical population, testing the effect of a tailored risk communication on individuals without cognitive impairment and exploring the efficacy of lifestyle and biological intervention interventions. The goal is to create an evidence-based framework for engaging individuals in proactive cognitive health management, bridging the gap between clinical research and preventive strategies in aging populations.

Required Skills

Background in medicine, psychology, cognitive science, biomedical science, engineering, biology, or related field. Interest in dementia, Alzheimer’s disease, dementia prevention, biomarkers and cognition. Familiarity with clinical research methodologies and statistical analysis (i.e. R) will be preferred.

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This project focuses on the analysis of brain positron emission tomography (PET) images in the context of Alzheimer’s disease pathology, namely amyloid-b plaques, neurofibrillary tau tangles, and neuroinflammation. This project adopts a multi-factorial protocol to assess the independent and synergic contributions of risk and protective factors to amyloid, tau, and neuroinflammation pathology, and neurodegeneration as assessed in vivo by PET, in determining specific disease risk and related clinical manifestation.

Required Skills

We welcome students with a variety of backgrounds and with a strong motivation to learn about neuroimaging techniques applied to neurodegenerative disorders. We offer the opportunity to learn how to process images, extract data, and analyse data from a research as well as a clinical perspective. Basic knowledge of programming is preferred, with familiarity with MATLAB and R, but not necessary. Please note that this project does not involve any work with patients directly..

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Image from https://das.org.sg/1501-adult-dyslexia/

Understanding how dyslexic readers learn and which modality facilitates the outcome and rate of their learning is key to improving teaching practice. For example, it has been proposed that learning through visual (text), visuo-kinetic or auditory information is more challenging for dyslexic readers than for typical readers. In this project, the masters student will investigate how the learning trajectories of participants with and without a diagnosis of dyslexia might differ on tasks using different modalities, and how this relates to neural metrics (MRI), in existing data. Tasks include on-screen text, spoken language stimuli and a visual-kinetic task. The student will compare learning trajectories between tasks and between reading groups, and how these differences relate to antomical or functional differences in key brain structures and/or to resting state activation patterns.

Required Skills

Interest in neuroscience, learning, and language

Basic knowledge of MRI acquisition and analysis.

A proactive attitude towards conducting comprehensive literature reviews to understand current research, and to identify gaps in the field.

Experience in programming (Python, MATLAB, R, Bash) is beneficial but not mandatory.

Willingness to learn programming and MRI analysis.

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Image credit https://www.newyorker.com/magazine/2018/09/03/the-mystery-of-people-who-speak-dozens-of-languages

The ability to speak several languages is a common and necessary occurrence in today’s globalised society. The fact that more and more people speak 2 or 3 languages on a daily basis is less and less surprising, but we are still fascinated by the existence of a few individuals in every century who show knowledge of dozens of languages. Hypepolyglots, people who speak more than 10 languages, are a fascinating case in multilingualism. Linguistic, cognitive and neuroscientific research on this population exists, although it is still limited. It has been suggested that this population differs significantly from other multilinguals in terms of their neural processing of language and in terms of the anatomy of key language areas. In this project, the student will examine in detail the profile of a hyperpolyglot who has been extensively tested on a range of language-related measures and who has undergone extensive structural and functional MRI. The case will then be compared with a selected control group.

Required Skills

Interest in neuroscience, multilingualism, cognition, and language. Priority to students with a background in psychology/neuropsychological testing and knowledge of psychological testing batteries.

A proactive attitude towards conducting comprehensive literature reviews, particularly with an interest in case studies and related methodology.

Basic knowledge of MRI acquisition and analysis.

Experience in programming (Python, MATLAB, R, Bash) is beneficial but not mandatory.

Willingness to learn programming and MRI analysis.

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Image from https://pmc.ncbi.nlm.nih.gov/articles/PMC7727365/

Neuroscientific research on polyglots, people who speak a large number of languages, is still limited. Evidence suggests that polyglots process language in a very similar way to other multilinguals with experience in a smaller number of languages, or even to mono- and bilinguals. They have been found to engage similar brain language areas during speech processing, but to a lesser extent. Our dataset includes a relatively large subsample of polyglots and hyperpolyglots who participated in an MRI session that included a language localiser in their first and second language. The masters student will replicate analyses from a previously published paper on a similar sample, and further explore the sources of group differences between (hyper)polyglots and other multilingual speakers. Depending on the student’s motivation and work progress, additional analyses including measures of motivation for language learning and additional MRI measures may be included in the project.

Required Skills

Interest in neuroscience and multilingualism.
Basic knowledge of MRI acquisition and analysis.
A proactive attitude towards conducting comprehensive literature reviews to understand current research and identify gaps in the field.
Experience in programming (Python, MATLAB, R, Bash) is beneficial but not mandatory.
Willingness to learn programming and MRI analysis.

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In collaboration with the team of Clara James (Geneva Musical Minds Lab)

This project aims to test the effectiveness of the “MRI Adventure” virtual reality application in reducing head motion during MRI scans in children. Using a dataset of 100 children who participated in the VR training before an MRI session, the study will compare their head motion and data quality during an 8-minute resting-state scan to a control dataset of 30 children who did not undergo the training. The outcomes will provide insights into the role of immersive VR experiences in improving data quality and the overall MRI experience for sensitive populations.

Required Skills

• Basic knowledge of neuroscience and MRI techniques.
• Interest in virtual reality applications and pediatric studies.
• Basic knowledge in Statistical analysis.
• Familiarity with data analysis tools (e.g., MATLAB, Python, or FSL) are a plus.

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This pilot project focuses on creating an EEG database to explore neural biomarkers of attention in neurotypical children. Thirty children (aged 6-10 years or 10-12 years) will participate in the study, which involves both resting-state and task-based EEG recordings. These tasks will assess different aspects of attention, including sustained attention, selective attention, and cognitive flexibility. By employing machine learning techniques, the project aims to identify reliable neural patterns associated with attention, paving the way for personalized interventions in future studies.

Required Skills

• Basic understanding of EEG recording and analysis.
• Interest or experience in machine learning applications in neuroscience.
• Familiarity with programming tools like Python or MATLAB.
• Strong analytical and organizational skills.

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This project aims to pilot a home-based neurofeedback (NFB) protocol for children using the Galea VR headset (https://galea.co), equipped with an embedded EEG system. The study will leverage a pre-developed VR classroom environment (initially designed for a VR cave) to simulate real-world distractions. The protocol will utilize the traditional theta/beta ratio as a key parameter for assessing and training attentional regulation. By engaging children in interactive tasks within the VR environment, this study seeks to evaluate the feasibility and initial effectiveness of the system in improving attention and focus. The outcomes will provide a foundation for extending this innovative intervention at home to address attention difficulties in pediatric populations.

Required Skills

• Basic knowledge of EEG and neurofeedback principles.
• Interest in pediatric populations and attention-related interventions.
• Programming experience with tools like Python or MATLAB for EEG data analysis is a plus.

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This project aims to create a set of standardized fMRI tasks targeting fundamental brain functions—motor, visual, auditory, and language—using PsychoPy, a powerful tool for creating experimental paradigms. The tasks will be designed to serve as clinical assessment tools for evaluating these basic functions in patients. The project involves coding the tasks, ensuring compatibility with fMRI protocols, and conducting a pilot test with a small group of patients to validate their effectiveness and usability. The outcomes will provide a reliable toolset for clinical use in assessing and diagnosing functional impairments.

Required Skills

• Experience with PsychoPy or other experimental design software.
• Basic understanding of fMRI paradigms and neuroimaging data collection.
• Programming skills (Python preferred) for task development.
• Interest in clinical applications of cognitive neuroscience and patient testing.

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Cerebrovascular reactivity (CVR) is the MRI signal response to a CO2 gas challenge and is used to assess vascular health in patients with cerebrovascular diseases (e.g., Moyamoya, small vessel disease). Data processing and modelling is required after acquisition in order to compute this quantitative map. The aim of this project is to improve the flow of acquiring such maps. This will require optimising the current Matlab/Python code in order to get CVR maps in less than 10 minutes after acquisition. During the project, you will learn about how research is carried out in a hospital and will be working within a team of MR physicists, neuro-scientists and computer-scientists from the CIBM Center for Biomedical Imaging and medical staff from the HUG.

Required Skills

• Scientific background with basic knowledge of MRI
• Experience with programming languages such as Python or Matlab
• Interest in clinical research

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Magnetic Resonance Imaging (MRI) is a non-invasive imaging technique known for exceptional soft tissue contrasts. However, its lengthy scans increase the likelihood of motion artifacts, degrading image quality and risking misdiagnoses. Manual inspection of the vast number of brain MRI scans in clinical and research settings is impractical, highlighting the need for automated quality control tools. This project seeks a motivated Master’s student to explore advanced image quality assessment using MRIQC, an open-source tool integrating many different image quality metrics (IQMs). The student will familiarize themselves with these IQMs and work hands-on with MRIQC. The goal is to apply it to motion-corrupted MRI datasets (both in vivo and simulated) to evaluate its ability to detect motion and quantify image degradation. This project offers an opportunity to contribute to cutting-edge MRI research, with the potential for inclusion in a scientific publication.

Required Skills

• Interest in MRI and image processing.
• Basic programming experience, ideally with Python/MATLAB (notions of Bash is a plus).
• Ability to work independently and proactively seek and apply relevant information.

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Making sense of linguistic input requires the combination of sequences of words into higher-order structures. The Dynamics of Brain and Language Lab runs experiments using EEG and MEG to track the neural processes that allow us to do this. We offer a prospective master’s student in Neuroscience the opportunity to carry out a study that uses recent methodological advances to probe structure building and representation. The project will focus on elucidating the neural timecourse of structure building and will provide insights into the cognitive constraints that limit the rate of information flow in human communication. 

For a student with a background in, or a profound interest in, linguistics and syntax, we are also able to offer a project with a focus understanding the neural implementation the grammatical function of modification by adjectives and adverbs. 

The lab offers a supportive and stimulating multidisciplinary environment for learning about neuroscience methods and the neural basis of language. We welcome applications from committed students whose backgrounds may not correspond perfectly to the profile described below.

Required Skills

• Interest in the cognitive neuroscience of language 
• Strong motivation to explore language processing and neural dynamics 
• Familiarity with methods of human cognitive neuroscience
• Hands-on experience of EEG
• Basic programming competence (Python, MATLAB) is advantageous  
• Experience with statistical tools (R, JASP) is beneficial but not required 

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The neocortex contains diverse synaptic circuits with specialized thalamocortical (TC) and corticocortical (CC) connections. In the mouse somatosensory cortex (S1), while CC inputs drive activity in pyramidal neurons, TC inputs rather evoke metabotropic glutamate receptor 1 (mGluR1)- mediated responses that are thought to modulate pyramidal neurons excitability. This project focuses on characterizing these synaptic inputs onto pyramidal neurons. Using conditional knockout mice, the student will study how the loss of mGluR1 affects synaptic and intrinsic properties. Techniques include viral vector injections, patch-clamp recordings, and validation of gene knockout. Time permitting, optogenetic stimulation will test how TC and CC responses are altered.

Required Skills

Neuroscience, Biology, Genetics, Physiology, Biomedical Sciences

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The higher-order thalamus (HOT) is throught to modulate cortical neuron excitability, potentially enhancing the integration of other long-range inputs. We hypothesize that connectivity patterns from HOT and other afferents are topographically organized on the dendrites of cortical neurons to support this modulatory mechanism. To study these synaptic arrangements, the student will use the eGRASP (enhanced Green fluorescent protein Reconstruction Across Synaptic Partners) method and high-resolution confocal microscopy to visualize HOT synapses and other inputs onto pyramidal neurons. Analytical tools will be developed to automate the detection and quantification of HOT connectivity patterns across different cortical neuron subtypes.

Required Skills

Neuroscience, Biology, Genetics, Physiology, Image analysis, Basic programming knowledge (MATLAB or Python).

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Supervisor: Céline Dürst, Ambizione fellow in Holtmaat lab

The rodent’s primary somatosensory cortex (S1) processes sensory information from the whiskers on the snout. During active sensation, cortical pyramidal neurons integrate feedforward and a variety of feedback inputs to form a percept. Our aim is to elucidate the spatiotemporal relationship between feedback and sensory-related synaptic inputs onto L2/3 cortical pyramidal neurons. Additionally, we will investigate the stability and plasticity of different feedback inputs to understand how their synaptic maps shape over time.

To address these questions, we will utilize multiple cutting-edge methodologies available in our laboratory, including:

• Expression of a presynaptic light-sensitive opsin in axonal afferents to optically activate long-range projections to S1.
• Use of a postsynaptic genetically encoded glutamate indicator to visualize activated excitatory synapses.
• Stereotaxic injections to transfect mice brains with the necessary viruses.
• Functional synaptic 2-photon imaging.
• Optogenetics.

In this project, the student will learn to perform these techniques and gain hands-on experience in advanced neuroscience research.

Required Skills

Some basics in programming (of any language) is strongly recommended and familiarity with neuroscience research techniques is a plus.

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Goal-directed movements are essential for our daily lives, yet the role of cortical neurons in controlling these actions remains unclear. Using advanced two-photon imaging and holographic optogenetics, we will “replay” recorded neural activity to test whether stimulating functionally identified neurons alters attention or motor output. High-speed motion capture will allow us to quantify the impact on these behaviors. We hope that this innovative approach will not only transform our understanding of cortical activity in movement control, but also inspire advancements in neuroprosthetics.

Required Skills

We are looking for students curious about functional motor control with a keen interest in optical techniques, neuroprosthetics and talented for hands-on laboratory work, including animal handling. Good programming skills are a plus.

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Genital sensory receptors are intimately linked to sexual functions and behaviours and their activation can trigger sensations of extraordinary pleasure, or discomfort, unlike any other area of our body. The clitoris is one of the most densely innervated somatosensory regions, containing thousands of sensory afferents and associated receptors. Yet, the neuroanatomical basis of the genital touch system, particularly the clitoris, remains surprisingly understudied. Your role: Join our group to investigate the anatomy, neurophysiology, and behaviours related to genital touch sensation and sensorimotor functions. You will be involved in an extensive team effort characterising the anatomy and physiology of genital sensory pathways, tracing the connections from peripheral sensory receptors in the genitals to the cortical representations. To examine how different processing centers along this ascending axis contribute to genital sensorimotor functions we will use portable miniature microscopes and electrophysiology in combination with cutting-edge histological clearing and imaging methods. You will also have the opportunity to work closely with our clinician gynecology collaborators at the Hôpitaux Universitaires de Genève as part of the translational aspects of this project.

Required Skills

We seek students with a keen interest in the somatosensory and sensorimotor systems, particularly in touch signals related to sexual and social behaviors. Candidates should bring creativity, enthusiasm for collaborative and hands-on laboratory work. Proficiency in R, MATLAB, or Python, as well as good French skills, are advantageous.

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Music is a universal cultural cornerstone, yet it remains only partially accessible to those with hearing impairments. Over the past few years, we have developed haptic devices to enable musical experiences through touch. Our research reveals that the sense of touch, like vision and hearing, has unique sensitivity thresholds requiring personalized calibration for optimal vibration-based stimuli. In this project we will develop innovative and cutting-edge approaches to measure these individualized thresholds by assessing both behavioral responses and neurophysiological correlates, thereby defining the perceptual limits of vibration-based stimuli. This project aims to objectively study tactile perception using psychophysical testing and neuroimaging (EEG/MEG) in human subjects. The student will design experiments to measure perceptual thresholds, record neural data during vibrotactile stimulation, and analyze music-related neural correlates.

Required Skills

Applicants should have a keen interest in cognitive neuroscience, proficiency in French and English, and good programming skills (MATLAB, Python, or R). Familiarity with basic hardware and musical or engineering skills are a plus.

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Primates are among the most highly encephalized creatures, with their large brains supporting advanced cognitive functions and behavioral skills. Mouse lemurs (Microcebus murinus), a prosimian primate endemic to Madagascar, present a unique opportunity to study primate neuroethology. As an early stem primate, their relatively small size and large brain-to-body ratio make them a compelling model for investigating the relationship between neural architecture, cognitive abilities, and behavioral strategies. Understanding how mouse lemurs forage within their natural habitat and laboratory conditions provides critical insights into the ecological and neural factors influencing their decision-making and adaptability.

This project investigates the foraging strategies of mouse lemurs, integrating field observations in their natural habitat with controlled neurophysiological experiments in laboratory settings. Using EthoLoop, a novel behavioral tracking framework, we will explore how environmental complexity and resource distribution influence their decision-making and adaptability. The study aims to characterize foraging behaviors, examine their neurophysiological basis, and provide insights into the evolutionary drivers of cognitive complexity. Findings from this research have broader implications for conservation and understanding the ecological and neural factors underlying primate adaptability.

Required Skills

We seek students with a keen interest in primate neuroethology. Candidates should bring creativity, enthusiasm for collaborative and hands-on laboratory and field work. Proficiency in programming is important, and engineering skills are a plus.

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In the present study we aim to analyze the joint effects of early Music and Maternal Voice Interventions on preterm infant’s brain and physiology through an innovative research paradigm.

Preterm birth is one of the leading causes for neurodevelopmental delay in surviving infants ¹, and has been associated with a wide range of behavioral and cognitive problems from childhood to adult life. Innovative early healthcare interventions for reducing the short and long-term impact of neonatal hospitalization have been tested in the neonatal period.

Required Skills

The Master student should enjoy to communicate with families and babies and speak the French language fluently, have interest in EEG, physiological and MRI data analysis.

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The cerebral cortex consists of dozens of different neuronal cell types that assemble during development to form the circuits underlying high-order functions such as cognition and language. These neurons, distributed across specialized cortical areas like sensory, motor, and associational cortices, undergo crucial postnatal maturation in response to environmental signals. Join our ongoing project to map cell-type specific developmental programs using cutting-edge techniques. You’ll help identify key genetic pathways during pre- and post-natal development of the mouse cerebral cortex and investigate how manipulating gene expression affects brain maturation and environmental adaptation.

Your Impact: You’ll contribute to understanding how the brain develops and adapts, with potential implications for cognitive development and neurological conditions.

Required Skills

What We’re Looking For:

• Curiosity about functional genetics and brain development
• Interest in learning single-cell RNA sequencing and bioinformatics
• Enthusiasm for hands-on laboratory work
• Basic R programming knowledge is beneficial but not required

Techniques You’ll Learn:

• In vivo gene manipulation
• Histological approaches including 3D analyses in clarified brains
• Bioinformatics
• Single-cell sequencing

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This project pertains to the “Art & Development” (https://www.hesge.ch/heds/rad/projets/art-developpement) randomized controlled trial in cognitive neuroscience, a Science National Science Foundation project (SNSF 214977, “ORBIT”, of which the protocol has been published in Open Access (https://doi.org/10.1186/s12906-024-04433-1).

We seek 2–3 motivated master’s students to join our interdisciplinary neuroscientific team on the ORBIT randomized controlled trial (SNSF 214977; https://doi.org/10.1186/s12906-024-04433-1).This large-scale study, involving 130 children aged 6 to 8 years at the outset, aims to evaluate the impact of two-year art interventions on the development of executive functions (EF), with a particular emphasis on working memory as a cornerstone of EF, in relation to behavior  and brain function and structure. Children were randomized into three intervention groups: Orchestra in Class, Visual Arts, and Cultural Outings (Control Group).

The research focuses on assessing the long-term effects of art interventions by disentangling the additional stimulation from artistic enrichment from the natural development occurring simultaneously through the standard curriculum. As part of your role, you will learn to analyze one or more modalities of brain Magnetic Resonance Imaging (MRI) data using standard neuroimaging pipelines. These modalities include grey matter morphometry, white matter structural connectivity, working memory task-related neural activations, and/or resting-state functional connectivity, with the choice depending on your affinity and background and the need of the project.

For those who are interested, you may receive training to participate in data collection at the two-year timepoint, including both MRI and behavioral assessments in children. Behavioral assessment requires perfect mastery of the French language.

Required Skills

• Interest in development, brain plasticity induced by learning, interventions
• Basic knowledge of neuroscience and MRI techniques
• Basic knowledge of Statistical analysis
• Familiarity with data analysis tools (e.g., MATLAB, SPM) is a plus

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This project pertains to the “Art & Development” (https://www.hesge.ch/heds/rad/projets/art-developpement) randomized controlled trial in cognitive neuroscience, a Science National Science Foundation project (SNSF 214977, “ORBIT”, of which the protocol has been published in Open Access (https://doi.org/10.1186/s12906-024-04433-1).

There is ongoing debate about whether being bilingual enhances executive functions, such as working memory, inhibition, and cognitive flexibility, in children. While earlier studies suggested that bilingualism might cause cognitive overload, potentially delaying development, a recent meta-analysis of 147 studies strongly supports the idea that bilingualism enhances executive functions. This “bilingual benefit hypothesis” posits that bilingual individuals have a behavioral advantage in executive function tasks due to the extensive use of brain networks involved in language control.

Some researchers propose that an executive function brain network orchestrates language control, and the regular engagement of this network in bilinguals leads to improved performance in tasks requiring executive functions. This project seeks to unravel the neural mechanisms underlying the effects of language experience on executive cognitive function.

We are seeking 1 motivated master’s student to contribute to this investigation. The student will learn to use data science and statistical methods to explore the relationships between the degree of multilingual experience, language proficiency (assessed using the LEAP-Q questionnaire), and performance on a battery of 12 executive function tasks. The analysis will focus on baseline data from the ORBIT project (SNSF 214977; https://doi.org/10.1186/s12906-024-04433-1), which includes data collected from 130 children aged 6–8 years in Geneva, a city with high linguistic diversity.

Additionally, the student will analyze neuroimaging data, specifically focusing on resting-state functional connectivity. This will involve isolating executive network connectivity and evaluating its dynamics in relation to multilingualism and executive function performance.

For those who are interested, you may receive training to participate in data collection at the two-year timepoint, including both MRI and behavioral assessments in children. Behavioral assessment requires perfect mastery of the French language.

Required Skills

• Interest in development, language, and executive functions
• Basic knowledge of neuroscience and MRI techniques
• Basic knowledge of Statistical analyses
• Familiarity with data analysis tools (e.g., MATLAB, SPM) is a plus

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This project pertains to the “Art & Development” (https://www.hesge.ch/heds/rad/projets/art-developpement) randomized controlled trial in cognitive neuroscience, a Science National Science Foundation project (SNSF 214977, “ORBIT”, of which the protocol has been published in Open Access (https://doi.org/10.1186/s12906-024-04433-1).

This master project involving 1-2 students investigates how art-based interventions influence the development of spatial working memory (WM) and its neural correlates in primary school children (6-8 years at start). As part of the ORBIT randomized controlled trial (SNSF 214977; doi: 10.1186/s12906-024-04433-1), children engage in one of three activities—Orchestra in Class, Visual Arts, or Cultural Outings (Control group) —over two years. Using Dynamic Causal Modeling (DCM) on fMRI data collected during a visual working memory N-back task, the project aims to assess changes in effective connectivity within brain regions associated with spatial WM. Baseline, one-year and two-year follow-up data will be analyzed and compared to examine age-related differences and intervention-specific effects on frontoparietal and cingulo-opercular networks, providing new insights into the neurodevelopmental impacts of art interventions.

For those who are interested, you may receive training to participate in data collection at the two-year timepoint, including both MRI and behavioral assessments in children. Behavioral assessment requires perfect mastery of the French language.

Required Skills

• Basic knowledge of neuroscience and MRI techniques.
• Interest in pediatric populations.
• Basic knowledge in Statistical analysis.
• Familiarity with data analysis tools (e.g., MATLAB, SPM) are a plus

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This project pertains to the “Art & Development” (https://www.hesge.ch/heds/rad/projets/art-developpement) randomized controlled trial in cognitive neuroscience, a Science National Science Foundation project (SNSF 214977, “ORBIT”, of which the protocol has been published in Open Access (https://doi.org/10.1186/s12906-024-04433-1).

This master project involving 1-2 students explores the development of inhibitory control in primary school children and its association to structural brain connectivity (white matter). As part of the ORBIT randomized controlled trial (SNSF 214977; doi: 10.1186/s12906-024-04433-1), children engage in one of three activities—Orchestra in Class, Visual Arts, or Cultural Outings (control group) —over two years. The research investigates the interplay between behavioral measures of inhibition—assessed through tasks such as Go/No-Go and Flanker—and brain connectivity patterns derived from resting-state fMRI. Central questions include whether response inhibition and interference control follow parallel developmental trajectories and how art-based interventions impact these processes. Advanced computational approaches, including Drift Diffusion Models (DDMs), will be applied to analyze decision-making parameters and their relationships with resting-state brain network connectivity after one and two years of interventions. This study aims to deepen our understanding of the brain mechanisms underlying the development of inhibitory control in young children and how they are influenced by structured artistic experiences.

For those who are interested, you may receive training to participate in data collection at the two-year timepoint, including both MRI and behavioral assessments in children. Behavioral assessment requires perfect mastery of the French language.

Required Skills

• Basic of cognitive neuroscience (inhibition and decision-making) and MRI techniques.
• Interest in pediatric populations.
• Basic knowledge in Statistical analysis.
• Interest in learning with MRI data analysis tools (e.g., MATLAB, Python or FSL).
• Interest in learning computational modeling (e.g., Drift Diffusion Models, R studio).

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This project aims to develop a battery of passive EEG (electroencephalography) headsets for testing language comprehension in 2–6-year-old children with (suspected) Developmental Language Disorder (DLD).

Developmental Language Disorder (DLD) is a neurodevelopmental condition of an unknown cause affecting approximately 7% of the population. According to a multilingual and multinational CATALISE consensus study (Bishop et al., 2016, 2017), DLD is an overarching term for a persistent language problem with a functional impact in daily life. DLD may impact behaviour, executive functions, academic achievement, and peer relationships.

The MSc student will administer a battery of questionnaires, behavioural and EEG tests of language comprehension to a sample of young children with (suspected) DLD, as well as obtain normative control data from typically-developing children. Mobile EEG headsets will be used.

The project will be based in Nina Kazanina’s lab in collaboration with Dr. Hélène Delage (FPSE, UNIGE).

Required Skills

• Initiative, energy and perfect organisational and people skills, including good contact with young children, and (near-)native knowledge of French are required.
• Prior experience with or interest in Electroencephalography (EEG) data acquisition and/or analysis is highly advantageous.
• Good data analysis and statistical skills are a plus.   
• Basic knowledge of linguistic concepts is expected. 

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This project investigates the neural mechanisms underlying the formation, storage, and retrieval of meaning in human language. Using neuroimaging and computational techniques, we aim to assess whether linguistic representations undergo spontaneous replay, akin to phenomena observed in other modalities like visual and motor stimuli.

Taking advantage of the hierarchical and structured nature of language, we will explore how the brain combines words into unified entities and whether merged elements are reactivated simultaneously. As one potential mechanism, we will test whether theta-gamma phase-amplitude coupling may support the storage of both content and order by nesting slow and fast oscillations. Overall, this project aims to broaden our understanding of the neural basis of human language and cognition.

In collaboration with Sophie Schwartz’s lab.

Required Skills

• Strong coding skills in Python are mandatory.
• Interest or experience in Magnetoencephalography (MEG) or Electroencephalography (EEG) data acquisition and/or analysis is highly advantageous.
• Familiarity with linear regression or more advanced machine learning models is a plus. 
• Basic knowledge of linguistic concepts is beneficial but not required.

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Remembering to take medication at a specific time or attend an important meeting later in the day are examples of delayed tasks that require maintaining intentions in memory. To ease this mental burden and increase the likelihood of success, people often rely on external aids such as alarms or to-do lists—a process known as intention offloading. Despite its importance for daily functioning, little research has explored this process, its underlying mechanisms, and its evolution across the lifespan. This project investigates the physiological basis of intention offloading and how it changes with age. By examining how aging affects the ability to shift between internal memory and external aids, the study integrates behavioral assessments and brain imaging to provide insights into these processes. The findings aim to support the development of interventions that enhance prospective memory strategies across the lifespan.

Required Skills

The ideal candidate should have a strong interest in cognitive neuroscience, particularly in memory and aging processes. We are looking for a highly motivated, creative, and proactive student who can contribute ideas and critically analyze scientific literature. Proficiency in both written and spoken English is essential, as well as strong interpersonal skills for working with human participants. Experience with programming is advantageous, particularly knowledge of PsychoPy, Matlab, SPM, and R for data analysis. Additional experience in analyzing functional magnetic resonance imaging (fMRI) data, psychophysiology, or computational modeling is also an advantage.

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Remembering to take medication at a specific time or attend an important meeting later in the day are examples of delayed tasks that require maintaining intentions in memory. To ease this mental burden and increase the likelihood of success, people often rely on external aids such as alarms or to-do lists—a process known as intention offloading. Despite its importance for daily functioning, little research has explored this process, its underlying mechanisms, and its evolution across the lifespan. This project investigates how aging affects the use of external reminders, focusing on two key factors: motivation and cognitive effort. Behavioral studies and physiological measures, such as cardiac pre-ejection period and systolic blood pressure, will explore age differences in how motivational factors influence reliance on reminders. The findings aim to provide insights into age-related changes in intention offloading and inform interventions to improve memory strategies across the lifespan.

Required Skills

The ideal candidate should have a strong interest in cognitive neuroscience, particularly in memory and aging processes. We are looking for a highly motivated, creative, and proactive student who can contribute ideas and critically analyze scientific literature. Proficiency in both written and spoken English is essential, as well as strong interpersonal skills for working with human participants. Experience with programming is advantageous, particularly knowledge of PsychoPy, E-Prime, Matlab, and R for data analysis. Familiarity with software for collecting psychophysiological data (e.g., BIOPAC or similar) is also beneficial. Additional expertise in analyzing psychophysiological data would be an advantage.

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Co-supervision Christophe Lamy/Céline Brockmann

The student will contribute to a research project within the Anatomy Department of the UNIGE Faculty of Medicine. The aim of this project is to enrich current knowledge of the neuroanatomy of the vulva, with particular emphasis on the innervation of the internal labia and the clitoris. The study is based on an approach combining several complementary techniques: cadaveric dissections, classical histology, immunohistology, as well as innovative 3D sample clearing methods enabling detailed three-dimensional analysis and various imaging techniques. The goal is to better describe the anatomical and histological basis of the sexual response, including sensory and erectile functions. Ultimately, this research will contribute to assessing the functional impact of labiaplasty, and to developing pedagogical tools designed for clinicians to inform and support patients in their treatment choices.

This project is developed in collaboration with the PhD project « Architecture fonctionnelle détaillée du clitoris » by Maéva Badré, within the framework of the Sciences, sexes, identités Program at the Faculty of Medicine.

Required Skills

• Interest in sexual health
• Interest in learning anatomy and histology
• Interest in science communication and public outreach

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We are offering a Master project to a motivated Master’s student to join our research team to undertake a project focused on understanding the effects of overeating on the neural circuits of the nucleus accumbens (NAc), hypothalamus, and the dopaminergic system. This project aims to elucidate how the dysregulation of feeding alters synaptic physiology, neural activity, and behavior through comprehensive in vivo and ex vivo methodologies. Specifically, you will examine how changes in dopamine and endogenous opioid signaling within the NAc impact reward processing and motivation, ultimately contributing to uncontrolled weight gain. In this project we seek to uncover why existing appetite-regulating drugs targeting homeostatic feeding systems fail to produce long-term weight loss and investigate whether the endogenous opioid signaling in the NAc could serve as a viable alternative therapeutic target.

Required Skills

We welcome applicants from a variety of scientific backgrounds and encourage motivated students to apply. While no specific qualifications are required, prior experience with rodent neuroscience techniques is advantageous. For those interested, there will be opportunities to gain training in advanced data analysis using MATLAB and Python, as well as the design and implementation of behavioral setups through 3D printing and Arduino programming.
Please note that this project involves significant hands-on animal work, including:

• Stereotactic surgeries
• Behavioral experiments and in vivo neural recordings and manipulations using fiber photometry, miniscope imaging, and optogenetics
• Perfusions and ex vivo histological analyses

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Insomnia Disorder (ID) is characterized by a chronic subjective sleep disturbance, with concomitant daytime impairment. 10% of the general adult population is concerned by this disorder representing a serious health issue. The current project aims to reduce insomnia severity, in ID patients, through the combination of imagery rescripting (IR), a technique where the individual is instructed to imagine a negative memory or image as vividly as possible, and to transform it into a positive one, and olfactory targeted memory reactivation (TMR) which consists of conditioning an odour with a positive mental scenario and then presenting the same odour during the night to reactivate the positive and relaxing scenario while being asleep. The outcomes of this RCT will allow us to investigate new and promising non-pharmacological treatments for ID.

The master student’s tasks will include the following tasks:

• Supporting the research team in the recruitment process
• Participating to material preparation and assessments (odours, EEG, cognitive tasks, etc.)
• Conducting interviews and experimental sessions
• Sorting and analysing behavioural, physiological and/or neuronal data

Required Skills

• Background in neuroscience or psychology preferred
• Interest in sleep studies, olfactory stimuli and clinical applications
• Familiarity with data analysis tools (e.g., MATLAB, Python, or R) is a plus.
• Proficiency in French is needed.

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In the proposed study, we take advantage of innovative non-invasive methods, in combination with serial awakenings, to study the subcortical and limbic activity (e.g., in the hippocampus, striatum, amygdala) during sleep and its relation to dreaming. These studies can provide a better understanding of the neural correlates of dreaming and consciousness in general, by studying a largely unexplored part of the brain in relation to these processes, and specifically, some important key structures of the limbic and subcortical system. Moreover, this study can offer for the first time causal (and not correlational) evidence for an emotional function of dreaming.

Required Skills

• Background in neuroscience, psychology, cognitive science, or a similar discipline.
• Basic knowledge of EEG data collection and analysis would be a plus.
• A strong interest in sleep, dreaming and consciousness.
• Prior experience with statistical tools such as R, Python and Matlab will be considered an advantage.

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Several studies suggest that mindfulness meditation can help adolescents and young adults reduce stress and anxiety, enhance attention, and decrease emotional reactivity. This project aims at analyzing resting-state fMRI data coming from two randomized controlled trials investigating the impact of an 8-week mindfulness training program on large-scale brain networks in adolescents (13-15 years old, Mindfulteen study) and young adults (health-care students, e-SMILE study).

Master’s students will have access to raw as well as preprocessed datasets, and will learn to apply dynamic functional connectivity analyses using the co-activation patterns (CAPs) technique. 

Required Skills

• Basic knowledge of fMRI acquisition and analysis
• Interest or experience in resting state fMRI analysis is highly advantageous.
• Experience with programming languages (MATLAB) and statistical tools (R).
• Interest in psychiatry and neuroscience

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This project aims to examine how several cognitive biases—such as the decoy effect and the endowment effect—collectively shape decision-making under uncertainty. Through a laboratory-based task that integrates behavioral and psychophysiological measurements, the study will induce these biases in real time to evaluate how they influence choices. The data collected will test a previously developed formal decision model, providing insights into the physiological bases and interactions of cognitive biases. By merging quantitative behavioral metrics with psychophysiological markers (e.g., heart rate, electrodermal activity), the research will deepen our understanding of bias formation and how it can be modeled computationally.

Required Skills

• Interest in interdisciplinary decision-making research
• Basic knowledge of experimental design, especially in a behavioral or cognitive neuroscience setting
• Willingness to learn experimental data collection methods involving human participants (e.g., pupillometry, EEG, fMRI, heart rate)
• Quantitative analysis / prior coursework or experience in statistics is a plus
• Curiosity and enthusiasm for bridging psychological, physiological, and computational perspectives

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This project investigates how teams solve complex logical and creative problems under hackathon-like conditions, focusing on psychophysiological markers of group synergy. Participants will complete established “collective intelligence” tasks—ranging from logical puzzles to creative challenges requiring empathy and theory of mind. By collecting physiological and/or neural data such as skin conductance (for arousal), heart rate variability (for stress regulation), and/or magnetoencephalography (MEG), the research seeks to uncover how group dynamics manifest in physiological states. Understanding these signatures may clarify how teams respond to high-pressure problem-solving scenarios and highlight key neural or physiological predictors of collective success.

Required Skills

• Interest in interdisciplinary decision-making research
• Basic knowledge of experimental design, especially in a behavioral or cognitive neuroscience setting
• Willingness to learn experimental data collection methods involving human participants (e.g., pupillometry, EEG, fMRI, heart rate)
• Quantitative analysis / prior coursework or experience in statistics is a plus
• Curiosity and enthusiasm for bridging psychological, physiological, and computational perspectives

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This project investigates how social cues shape decision-making under conditions of risk and uncertainty in individuals who routinely make critical choices (e.g., finance professionals, corporate managers). Participants will complete a task simulating a common-pool resource scenario with unpredictable returns, while social norms (“pro-sustainability” vs. “neutral”) are investigate. Using physiological measures and/or functional imaging, the study will examine how the brain balances potential rewards, social influence, and sustainability considerations. Findings will offer insights into the neural and psychological underpinnings of high-stakes decision-making, informing strategies that foster more sustainable choices in professional contexts.

Required Skills

• Interest in interdisciplinary decision-making research
• Basic knowledge of experimental design, especially in a behavioral or cognitive neuroscience setting
• Willingness to learn experimental data collection methods involving human participants (e.g., pupillometry, EEG, fMRI, heart rate)
• Quantitative analysis / prior coursework or experience in statistics is a plus
• Curiosity and enthusiasm for bridging psychological, physiological, and computational perspectives

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This project explores how principles of collective decision-making in social animals (e.g., ants, bees, or termites) might tap into evolutionarily older neural circuits when adapted for human teams. In addition to measuring behavior, the project will incorporate neural and physiological indices sensitive to subcortical or limbic engagement. For instance, pupillometry (which can reflect locus coeruleus–noradrenergic activity) and EEG-based event-related potentials (e.g., mid-latency components) may provide indirect windows into deeper brain processes. Complementary measures like skin conductance or heart rate variability can capture sympathetic and parasympathetic responses. By comparing these neurophysiological signals across conditions, the study aims to determine whether using animal-inspired strategies elicits distinct patterns of (sub)cortical engagement that promote collective success in uncertain environments.

Required Skills

• Interest in interdisciplinary decision-making research
• Basic knowledge of experimental design, especially in a behavioral or cognitive neuroscience setting
• Willingness to learn experimental data collection methods involving human participants (e.g., pupillometry, EEG, fMRI, heart rate)
• Quantitative analysis / prior coursework or experience in statistics is a plus
• Curiosity and enthusiasm for bridging psychological, physiological, and computational perspectives

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This project explores how individuals learn and make decisions in a complex environment where outcomes may be governed by both reward signals and uncertainty cues. The study involves a learning task designed to tease apart reward-based choice strategies (e.g., maximizing immediate gains) from uncertainty-based exploration (e.g., seeking information or reducing ambiguity). The behavioral data—such as choice patterns and response times—will be modeled using computational frameworks (e.g., reinforcement learning, surprise-based learning) to quantify the relative contributions of reward and uncertainty in guiding decisions. In parallel, basic psychophysiological measures (e.g., pupil dilation or heart rate variability) can be incorporated to index arousal or cognitive effort. Findings may help clarify the neural and computational underpinnings of how people balance exploitation versus exploration in uncertain environments.

Required Skills

• Familiarity with or willingness to learn reinforcement learning and/or other modeling techniques
• Basic programming skills (e.g., Python, MATLAB) for model implementation and data analysis
• Basic knowledge of experimental design, especially in a behavioral or cognitive neuroscience setting
• Willingness to learn experimental data collection methods involving human participants
• Quantitative analysis / prior coursework or experience in statistics is a plus

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How does the brain process virtual environments?

For decades, neuroscience has taken a reductionist approach to study brain and behavior. Virtual reality (VR) offers a new way to simulate realistic scenarios, allowing us to study naturalistic behaviors in a controlled lab setting. In this project, participants will engage in VR tasks while undergoing MRI scanning. By tracking brain activity, eye movements, behavioral performance, and physiological responses, we aim to uncover how the brain operates in immersive environments, bringing neuroscience closer to real-world applications.

Required Skills

• Passion for studying brain behavior in dynamic, realistic settings
• Basic programming skills (e.g., Matlab, Python) or willingness to learn
• Interest in eye-tracking

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What do reading and intuitive physics have in common?
Intuitive physics helps us understand the physical world, while literacy drives communication and learning. Both are essential cognitive skills requiring focused attention. This project hypothesizes a shared brain region for these two abilities. By combining neuroimaging and big-data approaches, we aim to map the functional and structural properties of this brain area. The findings could revolutionize education and diagnostics, shedding light on how these skills are acquired, stabilized, and decline over time.

Required Skills

• Interest in exploring brain-behavior relationaship
• Basic programming skills (e.g., Matlab, Python) or eagerness to learn
• Experience with or curiosity about behavioral and MRI experiments

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How do individual neurons shape our perception of the world?

The brain integrates information about objects, space, and attention to help us navigate and interpret our surroundings. Recent work by our lab has identified the posterior inferotemporal cortex (PIT) as a critical area for object-centered attention and perception. This project takes a deep dive into single-cell mechanisms by combining VR tasks with advanced techniques like single-cell recordings, pharmacological inactivation, and microstimulation in non-human primates. Our goal is to bridge cellular and human neuroscience to reveal the neural basis of vision and attention.

Required Skills

• Excitement for neuroscience at the cellular level
• Interest in animal research and experimental techniques
• Basic programming skills (e.g., Matlab, Python) or eagerness to learn

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Why do some stroke patients ignore one side of objects?

Visuospatial neglect is a disabling condition where stroke patients fail to perceive one side of space. While damage to the intraparietal sulcus (IPS) explains self-centered neglect, the neural basis of object-centered (allocentric) neglect is less understood. Recent work by our lab highlights the posterior inferotemporal cortex (PIT) as a key player in object-centered perception. Using state-of-the-art tools like virtual reality (VR), advanced neuroimaging, and behavioral analysis, this project will investigate the PIT’s role and its connections, aiming to uncover mechanisms that could inspire innovative rehabilitation strategies.

Required Skills

• Passion for understanding brain-behavior relationships
• Basic programming skills (e.g., Matlab, Python) or a willingness to learn
• Interest in eye-tracking and body-pose estimation

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This project investigates how the human brain consolidates memories during sleep using high-resolution 7T fMRI and EEG. It aims to deepen our understanding of sleep-dependent memory consolidation. By employing an immersive video paradigm that activates distinct cortical and hippocampal regions, we will track memory reactivation patterns during sleep. We hypothesize that the synchronization of these reactivations between the hippocampus and cortex during NREM sleep predicts memory consolidation performance on the following day. The study will involve healthy volunteers, each undergoing three MRI sessions at 7T, with one

Required Skills

• Interest in neuroimaging techniques
• Experience with statistics and programming (Matlab or Python)
• Basic knowledge of MRI analysis
• Strong interest in data science
• Proactive and eager to learn

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This project aims to understand how dysfunctional physiological sleep rhythms and networks contribute to memory impairment in temporal lobe epilepsy (TLE). Healthy subjects and patients with TLE will perform memory tasks before and after two afternoon naps at the Campus Biotech. We will use high-density EEG recordings and connectivity analyses to reconstruct the brain networks that encode the learning material and that produce sleep rhythms (spindles and slow waves). The overlap between these networks will be used to quantify the “memory replay” during sleep and will be compared in the two groups and correlated with their memory retention. Finally, we will use the two nap recordings to reconstruct resting state networks and validate the reliability of connectivity biomarkers of epilepsy as well as of classical sleep macrostructural variables. The student will contribute to the data collection, data analysis and interpretation of the results as well as to the related publications. S/he will learn about EEG pre-processing and analysis, electrical source imaging and brain network analysis.

Required Skills

Basic programming experience in Matlab or Python is highly desirable. Knowledge of statistical analysis and experience with EEG data collection/analysis is a plus.

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This project aims to design an interactive game tailored for individuals with memory loss and Alzheimer’s disease. The game will combine digital and mechanical elements to create an engaging, user-friendly experience that stimulates cognitive functions. Players will engage in memory-based and problem-solving tasks, designed to both entertain and rehabilitate. Each session will generate real-time data on the patient’s performance, allowing clinicians and caregivers to monitor disease progression. The game’s design will focus on accessibility, with simple controls and adaptable difficulty levels to suit different stages of the condition. By gamifying rehabilitation, the solution encourages consistent use while reducing the stigma around traditional therapeutic methods. Regular gameplay can provide early insights into cognitive decline or improvement, enabling timely intervention. The ultimate goal is to offer a fun, therapeutic tool that fosters mental engagement and supports personalized treatment strategies for Alzheimer’s patients.

Required Skills

• Innovative Mindset: A natural curiosity to solve real-world problems and the ability to think creatively in designing practical solutions.
• Game Design Knowledge: Familiarity with the principles of game design, including user experience, interactive mechanics, and engaging interfaces.
• Interest in Memory and Related Diseases: A genuine interest in exploring literature on memory, Alzheimer’s disease, and cognitive decline to understand patient needs and challenges.
• Willingness to Learn Programming: Motivation to acquire or improve programming skills, especially for prototyping the initial version of the game.
• Collaborative Attitude: Openness to working with clinicians, caregivers, and interdisciplinary teams to refine the game’s therapeutic value.

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We propose an approach to pediatric healthcare by deploying a thermal and visual camera atop the infant’s bed or stroller, we aim to continuously capture their thermal map during sleep, alongside visual video, tracking their movements, and through a microphone detecting the sounds emitted (including snoring, crying, etc.), and monitoring urination patterns. Through this comprehensive data collection, we plan in two different phases to use analytical methods and Artificial intelligence (AI-based models for discerning patterns associated with various diseases and health issues, thus enabling early prediction and intervention. The potential of this model is multifaceted, offering valuable insights into the health status of toddlers for both parents and healthcare professionals. For instance, consistent touching of the ears coupled with elevated thermal readings in that region may indicate a potential ear infection. Similarly, the detection of crying during urination, along with elevated temperature readings, could signify the presence of Urinary Tract Infections (UTIs), a common concern in pediatric care. Moreover, instances of coughing accompanied by high-pitched wheezing sounds during exhalation could alert to conditions, such as Bronchiolitis or respiratory distress, including asthma. By leveraging advanced deep learning algorithms, our model has the potential to analyze complex data streams from thermal imaging, visual imaging, and sound monitoring, thus providing early indications of various health issues in toddlers. This proactive approach facilitates timely interventions and empowers caregivers and healthcare providers with actionable insights for personalized pediatric.

Required Skills

• Motivation to Learn Programming for Image Analysis: A willingness to develop or enhance skills in Python, particularly in libraries and tools used for image analysis (e.g., OpenCV, scikit-image, or PIL).
• Interest in Literature Review: A proactive attitude towards conducting comprehensive literature reviews to understand current research and identify gaps in the field.

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