Currently available Master projects in SV or STI-IBI laboratories

This page contains a list of Master projects that are currently available in Life Sciences (SV) or in STI-IBI laboratories. These projects can be searched by keywords. If you are interested or would like to obtain further information from the Lab, please use the contact email listed.
Note that if you are interested in an interdisciplinary project (one where you will be co-supervised by two labs from different EPFL Schools), you can type ‘interdisciplinary’ in the search box.

 

Laboratory Title Category
Herzog Serial dependence in visual perception

Our recent experience has a strong impact on how we perceive the present: the daylight appears brighter after leaving a dark room, a white surface appears greener after staring at a red image. In some cases, prior events may even deceive us to perceive the present as more similar to the past than it actually is, a phenomenon known as serial dependence. The objective of this project is to investigate the role of prior experience and temporal dependencies in human vision by combining behavioral psychophysics and computational modeling. The student will be involved in the collection and analysis of psychophysical data with the primary objective to develop a physiologically plausible computational model of serial dependence in human vision.
Keywords: vision, computational modeling, psychophysics

Supervisor:  David Pascucci
Co-supervisor: David Pascucci   
Contact: [email protected]

Required: Good analytical and computational skills, ideal for students in: Computer Sciences, Life Sciences, Bioengeneering

Posted in 2020 
Neuroscience
Interdisciplinary
Herzog Temporal integration of visual information at multiple time scales

Perception involves the integration of sensory information at multiple time scales. In our research, we study how the visual system combines information over milliseconds and seconds to construct our conscious experience of the world. We use a multidisciplinary approach, combining human psychophysics, computational modeling, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Our final objective is to characterize the behavioral and neural correlates of the mechanisms that support the temporal integration of visual events at multilple level of processing, from perception to memory and decision-making. The student will have the opportunity to focus on one specific techique (e.g., psychophysics, EEG or fMRI) or a combination of techniques, and will be involved in the collection, analysis and modeling of different types of data.
Keywords: vision, computational modeling, EEG, fMRI

Supervisor:  David Pascucci
Co-supervisor: Michael Herzog, SV   
Contact: [email protected]

Required: Good analytical and computational skills, ideal for students in: Computer Sciences, Life Sciences, Bioengeneering

Posted in 2022 
Neuroscience
Interdisciplinary
Merten Development of 2nd generation droplet microfluidic single-cell RT-PCR chips

RT-PCR is a key step in single-cell analysis, including scRNAseq and paired sequencing of antibody encoding genes. However, its performance is limited by cellular inhibitory factors, which have a particularly high impact in low volume setting. The goal of the proposed master project is to overcome these limitations making use of advanced microfluidic and molecular biology approaches. During the project, the successful candidate will gain hands-on experience with microfluidic techniques and single-cell RT-PCR technology. In addition, the student will have the opportunity to become co-author and/or co-inventor on publications and patents resulting from the work. We are particularly looking for students with a background in molecular biology and a strong interest in interdisciplinary science. Prior expertise in microfluidics is a plus, but not absolutely required. Candidates are invited to send their applications by email to [email protected].
Keywords: microfluidics, single-cell analysis, RT-PCR

Supervisor:  Christoph Merten   
Contact: [email protected]

Required: Molecular biology and a strong interest in interdisciplinary science

Posted in 2023 
Molecular biology
Wet
Courtine Identification of Brain Reorganizations in Advanced Stages of Parkinson’s Disease to Unlock New Neuromodulation Designs

Neurorestore is a laboratory focusing on developing and applying medical therapies to restore neurological functions, integrating neuro-technologies with innovative treatments. Parkinson’s disease is currently incurable, and gait and balance impairments severely affect patients’ ambulation and independence with currently no clinical solution. To address this, the lab plans to use a transgenic mouse model of PD to identify new deep brain stimulation (DBS) targets that can alleviate these symptoms. The mouse model reproduces many aspects of progressive parkinsonism, including gait and balance impairments, allowing the lab to identify regions of interest with transcriptional activity and connectivity changes related to the emergence of these impairments. By surveying the entire brain and brainstem, the lab hopes to identify new DBS targets that can alleviate these symptoms. A Master’s student will have the opportunity to work with state-of-the-art techniques and models in neuroscience, whole-brain processing, behavioural task design, and analysis. The lab is seeking a person who can work on complex topics and assimilate multiscale information quickly. The Master’s student will help clarify the neuro-pathophysiology underlying gait and balance disorders in PD, leading to the development of effective DBS targets and treatments that will ultimately improve the lives of people with PD.
Keywords: deep brain stimulation, parkinson’s disease, whole-brain, gait

Supervisor:  Gregoire Courtine   
Contact: [email protected]

Required: immuno-histochemistry, data processing and analysis (Python, R and/or Matlab)

Posted in 2023 
Neuroscience
Wet
Courtine Transcutaneous Electrical Spinal Cord Stimulation in Rodents

The profound transformation in the lives of people suffering from spinal cord injury well known. Our laboratory has been working with animal models to better understand how electrical spinal cord stimulation and rehabilitation can help patients recover sensorimotor function, with the ultimate goal of improving treatment and enhancing the quality of life of patients. Transcutaneous Electrical Stimulation (TES) has emerged as a non-invasive method for stimulating the spinal cord. Numerous studies have been conducted to comprehend how this stimulation aids individuals with spinal cord injuries (SCI) in recovering sensorimotor functions. However, thus far, there is no biological evidence to substantiate such improvements. In this role, you will support our research efforts to unveil the underlying mechanisms behind this type of stimulation. As a valued team member, you will have the opportunity to contribute by conducting extensive histological work. Your responsibilities will include utilizing state-of-the-art, high-resolution microscopes (such as light sheet microscopy and confocal microscopy) to analyze neuronal tissue. Additionally, you will have access to cutting-edge machinery for tissue slicing. To further enhance your analysis, you will employ fluorescent antibodies to stain the tissue, and employ computational tools to quantify the results. By joining our team, you will be at the forefront of scientific innovation, making significant contributions to the field. This is an exceptional opportunity to collaborate with leading experts, gain invaluable hands-on experience with advanced research techniques, and be part of a project that has the potential to transform lives.
Keywords: spinal cord, rodent, stimulation

Supervisor:  Victor Perezpuchalt   
Contact: [email protected]

Required: Histology experience, Microscopy, Image analysis, Programming experience in: Matlab, Python and or Java and R

Posted in 2023 
Neuroscience
Dry and wet
Courtine Neuronal axon regenera-on to restore upper-limb functions after paralysis

Our team has managed to regenerate axons of propriospinal neurons across a complete spinal cord injury (SCI) for the first -me (Nature, MA Anderson et al. 2018). We currently focus on harnessing our regenerative therapy to recover neurological functions after SCI, including upper-limb movement, and to making it clinically applicable. The student will be involved in our effort to regenerate neuronal axons across a cervical SCI site to restore upper-limb functions after paralysis in rodents.
Keywords: regeneration, paralysis, SCI, upper-limb

Supervisor:  Achilleas Laskaratos   
Contact: [email protected]

Required: Preferred skills: previous research experience, cryostat tissue sectioning, immunohistological stainings, microscopy, image analysis, rodent perfusions, rodent animal work, R programing, biology and neuroscience knowledge

Posted in 2023 
Neuroscience
Wet
Bernier-Latmani Microdroplet-aided isolation of arsenic-methylating microorganisms from soil

Arsenic is a toxic element that is widely distributed in the environment and mainly occurs in inorganic compounds. However, some soil microorganisms can transform these into organic (methylated) compounds. This is problematic in agricultural soils such as in rice paddy fields because the methylated arsenic can be taken up by the plant, affecting crop health and food safety. Although arsenic methylation is known to be particularly strong in flooded (i.e., oxygen-deprived) soils, very little is known about this process e.g., which anaerobic microorganisms (bacteria, archaea) have this unusual phenotype, what controls it (biochemical pathway), what benefit the cell gains from it, etc. This has remained elusive because it is difficult to isolate these specific organisms from their habitat, and thus they have never been studied in the lab. Thus, we seek to isolate arsenic-methylating microorganisms from a paddy soil. To do so, we recently developed a novel approach combining droplet-based microfluidics, arsenic whole-cell biosensors and FACS. The goal of this project is to optimize and apply this approach to isolate and further characterize new isolates. You will use diverse tools including microfluidics, molecular biology, microscopy and bioinformatics.
Keywords: arsenic, biosensor, microdroplet, FACS

Supervisor:  Rizlan Bernier-latmani   
Contact: [email protected]

Required:  –

Posted in 2023 
Bioengineering
Wet
La Manno Developing a genomic deconvolution-based differential gene expression

The field of biostatistics heavily relies on Differential gene expression analysis (DGEA) for deciphering the environmental effects and associated condition shifts. However, the conventional approach using pseudobulk data, randomly sampled from cells, has limitations in capturing donor-specific signals when pooling cells from multiple sources. In this master’s project, you will utilize the power of genotype-based clustering technology, introduced by souporcell, to develop a new approach, single-cell donor deconvolution differential gene expression (scDDDGE). This strategy aims to enhance statistical power through donor-guided pseudobulk aggregation. Your primary tasks will include determining the number of donors in a dataset, performing cell deconvolution, and identifying differentially expressed genes (DEGs). Preliminary research indicates scDDDGE may outperform traditional aggregation methods, providing improved DEG identification accuracy, even when cell numbers and donor representation are minimal. The primary goal of this master’s project is contribute developing and use scDDDGE to study inter-donor treatment response and identify robust cell type markers. This project offers the exciting potential to boost statistical power in experimental designs and contribute to our understanding of interindividual variation in development.
Keywords: biostatistics, single-cell, computation, transcriptomics

Supervisor:  Gioele La Manno   
Contact: [email protected]

Required:  background in statistics and data science

Posted in 2023 
Computational Biology
Dry
Dal Peraro 3D density map embedding for cryoEM

Cryo-Electron Microscopy (cryoEM) has opened unprecedented vistas in biology, offering detailed insights into macromolecular architectures. CryoEM captures snapshots of large complexes in their native configurations, enabling scientists to visualise their three-dimensional structures with unprecedented precision. However, the raw data is inherently noisy, and converting these 2D images into accurate 3D reconstructions demands meticulous postprocessing and advanced computational techniques. However, the challenge persists in dealing with various global and local resolutions and deriving precise structures from the cryoEM density maps. Owing to the inherently dynamic nature of proteins and their potential interactions with ligands and other subunits within complex systems, the task of effectively juxtaposing experimental cryoEM data with established reference structures presents an ongoing challenge. This project aims to construct density map embeddings encapsulating the information in the voxel grid through an autoencoder and evaluate their biological meaningfulness. We seek a motivated Master’s student, with a solid background in computer vision and deep learning, and a keen interest in addressing challenges in the realm of structural biology and protein modelling. The project is a collaboration between the Laboratory for Biomolecular Modeling (led by Prof. Dal Peraro) and the Computer Vision Laboratory (led by Prof. Fua).
Keywords: computer vision, deep learning, cryoEM

Supervisor:  Matteo Dal Peraro
Co-supervisor: Pascal Fua (IC)   
Contact: [email protected]

Required: Data Science background (prior experience in Computer Vision would be prefered), with programming skills in Python, analytical skills and appetence for biology/bioengineering

Posted in 2023 
Computational Biology
Interdisciplinary
UPBARTH AI-based generative approaches for de novo protein design

AI-based approaches are revolutionizing structural biology and hold great promises for accelerating the discovery of therapeutics. However, despite tremendous advances, these methods have yet to generate de novo proteins for specific biomedical applications. To address these limitations, we are currently developing diffusion generative models to design de novo proteins with precise functions. We are seeking a masters student to work at the intersection of bioengineering and artificial intelligence. In this cutting-edge project, you will help develop and interrogate deep-learning models to design proteins with specific biomedical applications, with the ultimate goal of revolutionizing diagnostics and therapeutics. Your primary mission will be to help curate a comprehensive protein structural training dataset and assist in refining our existing deep learning models based on this dataset to generalize de novo protein design across the vast protein structure-sequence space, ultimately working towards solving one of synthetic biology’s greatest challenges. Working closely with the model’s author, you’ll receive one-on-one mentorship, and have the opportunity to improve your skills in biophysics, bioinformatics, AI and big data. Keywords: Protein design, deep learning, diffusion generative models, bioengineering, proteomics, personalized medicine Requirements: Proficiency in Python and bash is essential, while experience with protein structures and the PDB, along with knowledge of PyTorch and working on supercomputing clusters is highly relevant. Supervisor: Patrick Barth Contact: [email protected]
Keywords: Bioengieering, Protein design, deep learning, diffusion generative models, bioengineering, proteomics, personalized medicine

Supervisor:  Patrick Barth   
Contact: [email protected]

Required: Proficiency in Python and bash is essential, while experience with protein structures and the PDB, along with knowledge of PyTorch and working on supercomputing clusters is highly relevant.

Posted in 2023 
Computational Biology
Dry
Altshuler Molecular adaptation strategies of alpine snow microorganisms to warming

This project will involve isolation of alpine snow microbial members, followed by genomic analysis to determine their metabolic potential in cold and warming adaptation. This will be complemented with cultivation if isolates at a gradient of temperatures and isolation of their RNA/proteins to determine the molecular changes triggered by changes in temperature. This work will be used to asses the thermal tolerances of cryospheric microorganisms. This project will also include an bioinformatic and analysis component, but will heavily rely on initial lab-work. Depending on the preference of the student, there is also an option to incorporate filed work into the project.
Keywords: microbiology, gene expression, sequencing, culturing

Supervisor:  Ianina Altshuler   
Contact: [email protected]

Required: General laboratory experience is a bonus, some microbiological backround

Posted in 2023 
Molecular biology
Dry and wet
Altshuler Composition and function of crysphere microbiomes

This project will involve analysing in-house and publicly available metagenomes to determine their taxonomic compositions and functions and identify global genomic features of cryo-microbiomes. This would be followed up with a meta-annalysis to determine trends, patterns, and emergent qualities of microbiomes across different cryosphere environments.
Keywords: microbiology, sequence data, metagenomics, genomics, microbiome

Supervisor:  Ianina Altshuler   
Contact: [email protected]

Required: data analysis, working with large datasets, sequence analysis is a bonus

Posted in 2023 
Infectious diseases
Dry
Dal Peraro Graph neural network for prediction of protein-ligand binding affinity

In the realm of drug discovery, one crucial factor often determines the success or failure of drug candidates – their interaction with targeted proteins. Accurately predicting, quantifying, and interpreting protein-ligand interactions (PLIs) is paramount in pre-clinical drug development. While various experimental methods exist for PLI quantification, they are labour-intensive, time-consuming, and sometimes fall short in sensitivity, especially when dealing with proteome-wide interactions. Consequently, there’s a growing shift towards modelling methodologies as a complement or even replacement for experimental screening procedures. In recent years, the utilization of deep learning techniques has made significant inroads into drug discovery. Geometric deep learning and graph neural networks (GNNs) have emerged as particularly promising tools for capturing intricate relationships and hidden patterns in structural data, leading to successful applications in PLI prediction. However, encoding proteins and ligands into meaningful numerical features is not a straightforward task and demands meticulous design to achieve generalization power over unforeseen cases. The research proposal aims to refine and enhance a cutting-edge GNN methodology to predict PLIs by innovating in the design of protein and ligand descriptors, incorporating latest advances in graph neural network architectures, and employing graph explainability and inference techniques. We seek a motivated Master’s student, with a background in structural biology, pharmacology or bioengineering, and a keen desire to explore and deploy deep learning approaches. The project will be hosted at the Laboratory for Biomolecular Modeling (led by Prof. Dal Peraro).
Keywords: deep learning, graph neural network, chemoinformatics

Supervisor:  Matteo Dal Peraro   
Contact: [email protected]

Required: Background in structural biology, pharmacology or bioengineering. Proficiency in Python is required, as well as analytical skills and appetence for AI and deep learning (prior experience in data science would be appreciated but not mandatory).

Posted in 2023 
Computational Biology
Dry
Galland Biochemistry for the binding of bacterial cells with gold nanoparticles for Surface-Enhanced Raman Spectroscopy (SERS)

Bacteria enriched with gold nanoparticles emit very strong optical signals (Raman scattering) which allows for their detection and precise identification. Binding gold nanoparticles to bacteria (Figure 1) in a non-specific way is a relatively unexplored scientific avenue and requires the judicious selection of binding agents, as well as careful control of parameters such as pH, temperature and incubation times. The aim of the project will be to develop and optimize protocols for bacteria-gold adsorption focused on cost-efficiency, sustainability and ease-of-use. Evaluation of these protocols will be achieved through optical microscopy, Transmission Electron Microscopy (TEM) and SERS, among other techniques. Throughout the project, the student will work on chemically modifying gold nanoparticles to increase their affinity to bacteria while maintaining the stability of gold nanoparticles in aqueous solutions. This work will include a wide array of chemical work and techniques from gold nanoparticles synthesis, surface functionalization and ligand exchange, spectroscopy measurements (SERS & UV/Vis), Zeta potential measurements, TEM, bacterial culture and sample preparation, etc.
Keywords: nanoparticles, bactria, biochemistry, spectroscopy

Supervisor:  Marwan Elchazli   
Contact: [email protected]

Required:  –

Posted in 2023 
Bioengineering
Dry and wet
Galland Machine learning model development for the classification of bacterial strains using their Raman chemical fingerprint

Bacterial cells illuminated with a laser (for Raman spectroscopy) emit a characteristic “chemical fingerprint” which can be collected and analyzed with machine learning methods. Running this chemical fingerprint through machine learning algorithms allow us to determine the specific strain under observation. Available methods for this kind of application include PCA, LDA, Neural Networks, etc. The goal of the project will be to test different machine learning approaches, particularly neural networks-based techniques, to classify bacterial cells using their Raman scattering spectra. Throughout the project, the student will use pre-processing and machine learning methods in Python to work with large amounts of spectral data and classify them. They will be training and testing models and evaluating their effectiveness at classifying bacterial strains through a variety of metrics. Depending on the progress of the project, opportunities for publications might arise, which the student will be encouraged to participate in if they wish.
Keywords: bacteria, classification, machine learning, spectroscopy

Supervisor:  Marwan Elchazli   
Contact: [email protected]

Required:  –

Posted in 2023 
Computational Biology
Dry
Courtine Internship / Master project

Currently, externally applied spinal cord stimulation is being studied in order to understand its mechanisms. Clinical studies have shown that this technique is being effective in helping spinal cord injured patients to regain sensorimotor functions ( “Non-invasive spinal cord electrical stimulation for arm and hand function in chronic tetraplegia: a safety and efficacy trial” ). However, thus far, there is no biological evidence to explain such improvements. As part of our group, you will support our research efforts to unveil the underlying mechanisms behind this type of stimulation. We offer a rich and multidisciplinary project where your responsibilities will include performing histological work (e.g. tissue sectioning, immunostaining, in-situ hybridization). You will analyze these tissues using high-resolution microscopy and then analyze and quantify these images using computational tools, like QuPath or Fiji. You will also have the opportunity to become involved in electrophysiological studies assessing the muscular response in animal models due to different stimulation protocols. In parallel, depending on your interest and the availability of resources, you may also participate in numerical simulations, working on highly realistic biophysical animal models.
Keywords: Non-Invasive spinal cord stimulation in rodent model

Supervisor:  Victor Perezpuchalt   
Contact: [email protected]

Required: Previous histological experience, Microscopy experience (light sheet and/or confocal microscopes), Data analysis and Image processing, Signal processing, Programming experience in Python and Matlab (or other), Be comfortable to work with animals (rodents)

Posted in 2024 
Neuroscience
Dry
Courtine Master project : Robotic Platform for Upper Limb Experiments in Mice

To assess, evaluate and quantify improvements in motor function, robotic platforms are essential. A robotic platform has been developed previously with incomplete aspects that need to be taken into account. The main goal of this project is to finish the robotic platform and to fine tune the system to allow proper training with rats and mice. In terms of practical work, the student will perform : – Software implementation and optimization to control motors, deal with the communication between all the components, show data in real time and interact with user input through a touchscreen and save this data for future analysis. – Electronics: assemble different electronic components and modify, repair or create some parts, if needed. – Mechanics: modify, repair or create some parts if needed. This project will challenge you in various aspects of device development and prototyping, including the design of mechanical and electronic parts, manufacturing and assembling them using 3D printers and other classical manufacturing processes, programming the control of the robot and the communication of the different robot parts. The project will conclude with animal experiments to validate the robot (in collaboration with the supervisor).
Keywords: Robotic Platform for Upper Limb Experiments in Mice

Supervisor:  Victor Perezpuchalt   
Contact: [email protected]

Required: – Programming skills in: Python, C/C++, Matlab and/or Java – Electromechanical prototyping experience – 3D design using: SolidWorks, Catia, Creo PTC (or similar) – 3D printing experience – Previous rodent animal work or be able to work with animals

Posted in 2024 
Bioengineering
Dry
Mathis Deep learning-based neuro-musculoskeletal models for adaptive learning and control

This project aims to develop a comprehensive biomechanical model of the adult mouse, integrating neural and musculoskeletal dynamics for studying motor control and learning. The project will aim to bridge our newly developed forelimb model with whole-body models in a physics-based simulation environment, to create an in silico framework that will bridge neural activity with body dynamics. This output platform will enable the comparison of neural recordings from sensorimotor areas to computational predictions, offering new insights into how internal models of the body are represented and updated in the brain.
Keywords: Neuroscience, data science, deep learning, biomechanics

Supervisor:  Mackenzie Mathis   
Contact: [email protected]

Required: Programming in Python

Posted in 2024 
Bioengineering
Dry
Pioletti Investigation of the effects of degradation induced by different solutions on osteochondral tissue samples in vitro

Osteoarthritis affects approximately 500 million people worldwide, with more than 50% being over the age of 65. With the aging population, this number is expected to increase in the coming years. This disease can significantly reduce the quality of life by making movements painful and difficult. Affected individuals may experience difficulties in participating in daily, professional, or social activities, leading to psychological distress. Although there is currently no definitive cure for osteoarthritis, several treatments can help alleviate the symptoms. To test these different treatments and analyze the regeneration processes, it is necessary to have an in vitro osteoarthritic model, which involves controlled cartilage degradation. In this context, we aim to test certain degradation solutions at different concentrations in vitro to develop a relevant model. This is why this master’s project was initiated. Part 1: Development of the support for osteochondral samples The student will need to design and print a support to hold the osteochondral samples. This support must be functional and suitable for use in a cell culture environment. Part 2: Testing different degradation solutions The student will prepare the samples and test different degradation solutions on them, placed on the support developed in Part 1. Part 3: Analysis of degradation tests The student will perform biochemical and histological/ immunological analysis on the samples obtained from the degradation tests conducted in Part 2. (Additional tests may be carried out if necessary.)
Keywords: Design, 3D printing, explant culture, histology

Supervisor:  Dominique Pioletti   
Contact: [email protected]

Required: Some experience in culture and CAD tools

Posted in 2025 
Bioengineering
Dry and wet
Bunne Various Master projects

All Master projects available in the Bunne Laboratory are available (and updated) on this website.
Please make sure you have a look!

Supervisor:  Charlotte Bunne   
Contact: [email protected]

Posted in 2025 
Computational Biology
Dry
Biomedical Imaging Group Various Master projects

All Master projects available in the Biomedical Imaging Group (BIG) are available (and updated) on this website.
Please make sure you have a look!

Supervisor: Check details on the website linked above   
Contact: [email protected]

Posted in 2025 
Cell Biology
Dry and wet
Center for Imaging Various Master projects

All Master projects available in the Center for Imaging are available (and updated) on this website.
Please make sure you have a look!

Supervisor: Check details on the website linked above   
Contact: Check details on the website linked above

Posted in 2025 
Bioengineering
Dry and wet
Gallini Genetically engineering keratinocytes to express fluorescently tagged oncogenes

The generation of a construct for skin keratinocytes to simultaneously express multiple oncogenes or tumor suppressors tagged with distinct fluorophores, facilitating high-resolution in vitro and in vivo studies of cellular dynamics.
Keywords: Cloning, lentiviral infection, imaging

Supervisor:  Sara Gallini   
Contact: [email protected]

Required: Molecular Biology

Posted in 2025 
Cancer biology
Wet
McCabe Predicting early hallmarks of Amyotrophic lateral sclerosis (ALS)

Amyotrophic lateral sclerosis (ALS) is a deadly rare neurological disease of unknown etiology and for which no cure is available. In the lab, we use in vivo and in vitro models to try and understand molecular mechanisms underpinning the disease to develop therapeutic targets. This master’s project aims to determine synaptic alterations throughout the ALS progression and define early hallmarks predicting neurodegeneration. The study will be performed on groups of post- mortem murine spinal cords (naïve, carrying ALS mutations, and treated with gene therapy). The student will familiarize themselves with and master different histological techniques, microscopy, image reconstructions, and analyses.
Keywords: Neurodegeneration, synapses, histology, microscopy

Supervisor:  Samuel Vernon   
Contact: [email protected]

Required: –

Posted in 2025 
Neuroscience
Dry and wet
McCabe Production of ALS-like artificial CSF

Amyotrophic lateral sclerosis (ALS) is a deadly rare neurological disease of unknown etiology and for which no cure is available. In the lab, we use in vivo and in vitro models to try and understand molecular mechanisms underpinning the disease to develop therapeutic targets.
This master’s project aims to conduct a meta-analysis of proteomic results on cerebrospinal fluid (CSF) from murine ALS models and human patients. The student will learn to perform systematic research and integrate and re-analyze datasets with bioinformatic tools. Results will help produce ALS-like artificial CSF, and isolate proteins affecting the neural substrate.
Keywords: Neurodegeneration, proteomics, bioinformatics

Supervisor:  Samuel Vernon   
Contact: [email protected]

Required: –

Posted in 2025 
Neuroscience
Dry
McCabe Drosophila NMJ Ageing

Our lab has recently developed an adult Drosophila NMJ model to study behavioural, morphological and physiological hallmarks of ageing and degeneration in a high throughput model. This project will allow the candidate to utilize this preparation to study how modification of important synaptic proteins phenotypically manifest during age-linked synaptic degeneration. Due to the versatile nature of the preparation, the technical skills can adapt to the applicants specific interests and can encompass functional, morphological and/or behavioral techniques.
Keywords: Neurodegeneration, ageing, neuromuscular junction

Supervisor:  Samuel Vernon   
Contact: [email protected]

Required: –

Posted in 2025 
Neuroscience
Wet
McCabe Automated analysis of biological data sets

The goal of this project is to design and develop a robust machine learning-based software solution to automate the analysis of biological data sets. The applicant will leverage advanced algorithms to process, analyse, and extract meaningful insights from morphological, functional and/or behavioral data sets with a goal to reduce the time and effort required for manual data processing. The masters project can cover several of the below areas of development subject to the students experience:
Data Pre-processing: Develop and implement data cleaning, transformation, and normalization techniques to prepare raw data for analysis.
Model Development: Create and optimize machine learning models for tasks such as classification, regression, anomaly detection, and clustering based on the specific data requirements.
Testing & Validation: Implement rigorous testing, validation, and performance evaluation to ensure the reliability and accuracy of the system.
Keywords: Neuroscience, machine learning, post-hoc analysis

Supervisor:  Samuel Vernon   
Contact: [email protected]

Required: –

Posted in 2025 
Neuroscience
Dry
McCabe Optimising a novel drug-inducible transgenic expression system

The student will explore and refine a powerful new drug-inducible inhibitor of the UAS/GAL4 binary expression system, a cutting-edge tool in genetic studies. This system works like a remote control, allowing scientist to temporally control genetically encoded transgenics using a drug. This project will involve hands-on experiments such as:
Parameter optimization: media types, drug concentration.
Expression Validation: crossing flies with a drug inducible GAL80 repressor and fluorescent markers.
Generation of a methodological pipeline: Analyzing data to improve system efficiency and responsiveness.
Keywords: Genetics, tool development, optimization, neurogenetics

Supervisor:  Samuel Vernon   
Contact: [email protected]

Required: –

Posted in 2025 
Neuroscience
Wet
Radenovic Fluorescence Techniques for Exploring Biological Nanopore Mechanisms

Nanopore sensing is emerging as a valuable tool for the characterization & sequencing of DNA, RNA and more recently proteins. State of the art biological nanopores are already commercially available in devices like the minion (Oxford Nanopore Technologies), the fundamental physics around nanopores however are poorly understood. By studying the ionic flux and perform imaging experiments with those nanopores, we aim at understanding two mechanisms of biological nanopores: gating and oligomerization path. Two phenomena which, if understood properly, could lead to serious advancements in the field of biological nanopores.
Keywords: Biological nanopores, fluorescent and electron microscopy, protein engineering

Supervisor:  Aleksandra Radenovic
Co-supervisor: Matteo Dal Peraro   
Contact: [email protected]

Required: Basic lab work, commitment and excitement

Posted in 2025 
Bioengineering
Dry and wet
Merten Development of an electrical device to break droplet emulsions

Water-in-oil microfluidic droplets are commonly used to perform single cell assays and analyses for biomedical research. Traditional methods for recovering the contents of these droplets rely on chemical treatments, which can impede subsequent molecular analyses. Our project aims to evolve this process by creating an electrical apparatus that can efficiently induce droplet coalescence without compromising cell viability. Experience with prototyping, using electronic components, and working with laboratory equipment will be beneficial.
Keywords: Microfluidics, single-cell analysis, biomedical engineering

Supervisor:  Matteo Broketa   
Contact: [email protected]

Required: Basic lab work, commitment and excitement

Posted in 2025 
Bioengineering
Dry
Ramdya Behavioral studies in the laboratory

This project will provide hands-on experience working with flies in the laboratory. Specific goals of this project will depend on the needs of a particular research direction but will require an examination of how neural activation or other perturbations modulate animal behavior. The student will get the opportunity to design experiments and analyze their own data. This project in neuroscience will be supervised at the Neuroengineering Laboratory. Please email your CV, Bachelor (and if relevant Master) grades.
Keywords: Neuroscience, behavioral science

Supervisor:  Pavan Ramdya   
Contact: [email protected]

Required: Python

Posted in 2025 
Neuroscience
Dry and wet
Ramdya Use computational tools to study neural circuit activity

A central goal of neuroscience is to link neural activity and behavior. The objective of this project is to use computer vision and machine learning-based approaches to extract neural activity patterns during behavior and to make accurate predictions relating behavioral and internal states. This project at the interface between computer science and neurobiology will be supervised at the Neuroengineering Laboratory. Please email your CV, Bachelor (and if relevant Master) grades.
Keywords: Computer vision, machine learning, neuroscience

Supervisor:  Pavan Ramdya   
Contact: [email protected]

Required: Python and/or C/C++

Posted in 2025 
Computational Biology
Dry
Ramdya Use computational tools to study animal behavior

Insects generate complex behaviors even with a numerically small nervous system. But what exactly is a ‘behavior’? How can we quantify and segment individual actions from one another? The objective of this project is to leverage computational and machine learning approaches to study the behavior of flies. These behavioral sequences can then be linked to simultaneously acquired neuroimaging data. This project at the interface of computer science, and neuroscience will be supervised at the Neuroengineering Laboratory, possibly in close interaction with computer science laboratories on campus. Please email your CV, Bachelor (and if relevant Master) grades.
Keywords: Machine learning, computer vision, animal behavior, neuroscience

Supervisor:  Pavan Ramdya   
Contact: [email protected]

Required: C/C++, and/or Python

Posted in 2025 
Computational Biology
Dry
Ramdya Study the full graph (connectome) of the fly nervous system

It has recently become possible to analyze the connectome (a graph representing the connections between every neuron in the brain or motor system of the fly. The goal of this project is to further improve and explore this graph to answer specific questions about neural circuit motifs underlying complex motor behaviors. This project at the interface of computer science, mathematics, and biology will be supervised at the Neuroengineering Laboratory. Please email your CV, Bachelor (and if relevant Master) grades.
Keywords: Network analysis, neuroscience

Supervisor:  Pavan Ramdya   
Contact: [email protected]

Required: Python

Posted in 2025 
Neuroscience
Dry
Ramdya Use robotic systems to automate biological experimentation

Biological experiments often require skill and extensive training. They can also be repetitive. The objective of this project is to democratize and automate experimentation by developing automated vision-guided robotic systems that perform behavioral and/or neural studies. This project at the interface between robotics and neurobiology will be supervised at the Neuroengineering Laboratory, possibly in collaboration with the Microrobotics Laboratory. Please email your CV, Bachelor (and if relevant Master) grades.
Keywords: Robotics, neuroscience

Supervisor:  Pavan Ramdya   
Contact: [email protected]

Required: Robotics / electronics, programming experience

Posted in 2025 
Bioengineering
Dry and wet
Ramdya Build a robotic fly

Nature has solved numerous challenges associated with autonomous behavioral control. We hope to leverage these solutions in robotics. The goal of this project is to construct an insect-inspired robot and to test bioinspired algorithms of limb control. This project at the interface of robotics and biology will be supervised at the Neuroengineering Laboratory in collaboration with EPFL robotics groups. Please email your CV, Bachelor (and if relevant Master) grades.
Keywords: Robotics, biomechanics, control

Supervisor:  Pavan Ramdya   
Contact: [email protected]

Required: Microfabrication and/or Electronics experience

Posted in 2025 
Bioengineering
Dry and wet
Protein Production and structure Core Facility High-Throughput Cell-Free Expression for protein designs

This master’s project aims to overcome the limitations of traditional protein production methods using living cells, such as E. coli, mammalian, or insect cells. With a growing number of in silico designed proteins requiring rapid testing for expression and folding in 96/384-well formats, the most efficient solution is in vitro cell-free expression, which is easily automatable. The project will focus on developing new cell-free expression systems, including mammalian cell extracts, to improve protein folding accuracy. Additionally, it will establish a streamlined workflow from cell-free expression to binding assays, accessible to the EPFL community. This work will be carried out in collaboration with four research labs from the SV-STI faculties and one EPFL spin-off start-up, contributing to more efficient and scalable research in protein engineering and fostering innovation within the EPFL ecosystem.
Keywords: Protein design, biotechnology, cell-free expression, target validation

Supervisor:  Florence Power
Co-supervisor: Kelvin Lau   
Contact: [email protected]

Required: Wet lab experience in molecular biology

Posted in 2025 
Bioengineering
Wet
Antanasijevic Structural analyses of antibody-antigen interactions in the context of viral infections

Electron microscopy allows to visualize antibody-antigen interactions with atomic-level precision which enables detailed studies of the underlying molecular mechanisms governing the immune response in the context of infection or immunization. In our lab we use these tools to identify common immunogenic signatures shared by individuals and develop algorithms for prediction and modulation of antibody-mediated immunity. Pathogens of interest include HIV, yellow-fever virus, dengue, rabies, SARS-CoV-2, and enteroviruses, while the matching antibody samples are obtained from human and animal sources. Specific antibody-antigen system for the Master Thesis project will be selected based on student’s preferences and sample availability. The project typically includes the biochemistry component (“wet lab”), imaging on state-of-the-art electron microscopes and data processing using HPC resources on campus (“dry lab”).
Keywords: Virus, antibody, immunity, cryoEM

Supervisor:  Aleksandar Antanasijevic   
Contact: [email protected]

Required: Strong background in Biological Chemistry, Chemistry and Physics, Participation (or intention to participate) in the BIO-315 course on Structural Biology

Posted in 2025 
Infectious diseases
Dry and wet
Correia Molecular Docking with Reinforcement Learning

Molecular docking is notoriously challenging due to the extensive conformational space and low amount and quality of the available experimental data. Current methods often rely on traditional genetic algorithms or hill climbing, which makes them computationally expensive and limited in flexibility. Effectively, no existing solution is able to address molecular docking in a generalizable way. Given the similarities between docking and manipulation tasks in robotics, as well as advancement of reinforcement learning in control problems, we plan to introduce a reinforcement learning agent to explore the continuous action space of molecular torsion angles and rigid body transformations. This approach requires no training data and allows us to use customizable scoring functions based on first principles or established force fields. With possibility to train an agent on the entire proteome (i.e. using millions of AlphaFold models), the aim of this work is to address the existing generalizability problem in molecular docking and develop a more efficient and robust alternative to the traditional docking algorithms.
Keywords: Machine learning, reinforcement learning, molecular docking, drug discovery

Supervisor:  Bruno Correia   
Contact: [email protected]

Required: Machine learning background, good knowledge of Python and PyTorch. As a plus: experience in Reinforcement Learning and/or Robotics.

Posted in 2025 
Computational Biology
Dry
Petersen Analysis of animal behaviour during learning

In laboratory settings, video filming is used to monitor movements during behavioural paradigms. Machine learning tools, such as DeepLabCut, an open-source software tool for pose estimation, have facilitated the analysis and quantification of behaviour. Recent work has emphasized the influence of movement on neural activity as well as the richness of movement patterns that animals exhibit. The goal of this project is to explore and quantify orofacial movements of head-fixed mice that perform an associative learning task. More precisely, we seek to model possible relationships between movements and mouse decisions as the mouse learn the association of a sensory stimulus with a reward on a trial-by-trial basis.
We are looking for an outstanding and highly motivated student to help us in the data analysis. The project will enable the student to manipulate rich behavioural data and implement advanced analysis methods. This project requires advanced programming skills (Python), experience with machine learning, statistics, data visualization and an interest for neuroscience and behavior.
Keywords: Reward-based learning, behavior, mice

Supervisor:  Carl Petersen
Co-supervisor: Axel Bisi   
Contact: [email protected]

Required: Python data analysis

Posted in 2025 
Neuroscience
Dry
Petersen Analysis of brain-wide neuron-to-neuron interactions during learning

The mouse brain is a complex network of millions of neurons, each receiving and projecting signals to hundreds of other neurons. Recent advances in recording technologies allowed us to measure thousands of single neurons simultaneously during an associate learning task. Our unique large-scale dataset is amenable to the analysis of the concerted activity of many neurons in various brain regions during behaviour. The goal of this project is to quantify single-neuron interactions at a brain-wide scale, and explore how learning may shape these interactions.
We are looking for an outstanding and highly motivated student to help us in the data analysis. The project will enable the student to manipulate large datasets of spiking activity and implement advanced analysis methods. This project requires advanced programming skills (Python), experience with signal processing, statistics, data visualization and an interest for neuroscience and behavior.
Keywords: Reward-based learning, mice, neuronal code

Supervisor:  Carl Petersen
Co-supervisor: Axel Bisi   
Contact: [email protected]

Required: Python data analysis

Posted in 2025 
Neuroscience
Dry
Petersen Analysis of brain-wide neuronal selectivity during learning

Understanding how individual neurons encode information is fundamental to deciphering brain function. Single-neuron measures of selectivity provide a mean to characterise how neurons respond to particular stimuli or task-related events, providing insights on the neural mechanisms underlying behaviour. The goal of this project is to quantify and model single-neuron measures of selectivity at a unique brain-wide scale, and explore how learning may shape this selectivity.
We are looking for an outstanding and highly motivated student to help us in the data analysis. The project will enable the student to manipulate large datasets of spiking activity and implement advanced analysis methods. This project requires advanced programming skills (Python), experience with GLMs, machine learning, statistics, data visualization and an interest for neuroscience and behavior.
Keywords: Reward-based learning, mice, neuronal code

Supervisor:  Carl Petersen
Co-supervisor: Axel Bisi   
Contact: [email protected]

Required: Python data analysis

Posted in 2025 
Neuroscience
Dry
Altug MS Thesis/Internship Opportunity: Development of nanophotonic biosensors for detecting environmental contaminants

Nanophotonic technologies are revolutionizing the field of biosensing, enabling the development of highly sensitive, cost-effective, and field-ready biosensors. At EPFL’s BIOS Laboratory, a patented nanophotonic platform has been developed to detect biomolecules with exceptional precision and speed. This project focuses on adapting the technology for environmental applications, aiming to detect small molecule contaminants.
The work will involve designing innovative bioassays, integrating them with microfluidic systems for efficient sample processing, and implementing multiplexed detection to allow simultaneous testing of multiple contaminants. The goal of the project is to extend the capabilities of this biosensing platform to water monitoring. The project will also provide a unique opportunity to have interactions and collaborations with an early stage start-up (NEOSENS) that aims to tech transfer this powerful technology for real-world applications.
Keywords: Biosensing, nanophotonics, bioassay development, environmental contaminants, tech transfer

Supervisor:  Abtin Saateh
Co-supervisor: Marco Fumagalli   
Contact: [email protected]

Required: Python data analysis

Posted in 2025 
Bioengineering
Dry and wet
Gönczy Engineering a modified centriole duplication cycle in human cells

Objective: engineer and analyze centriole duplication cycle with altered organelle number control.
Approaches: molecular and cell biology, expansion microscopy, super-resolution microscopy, live imaging.
Ideal for students in: Life Sciences, Bioengineering.
Keywords: Molecular biology, cell biology, centriole, microscopy

Supervisor:  Pierre Gönczy   
Contact: [email protected]

Required: –

Posted in 2025 
Cell biology
Wet
Gönczy Analyzing novel centriolar proteins in Chlamydomonas reinhardtii

Objective: identify localization and test function of novel centriolar proteins in the green alga Chlamydomonas reinhardtii.
Approaches: CRISPR/Cas9-mediated GFP tagging, as well as disruption, of novel centriolar proteins in Chlamydomonas reinhardtii, expansion microscopy, super-resolution microscopy.
Ideal for students in: Life Sciences, Bioengineering.
Keywords: Cell biology, centriole, CRISPR/Cas9, microscopy

Supervisor:  Pierre Gönczy   
Contact: [email protected]

Required: –

Posted in 2025 
Cell biology
Wet
Gönczy Investigate mechanisms of centriole elimination using C. elegans

Objective: discover mechanisms regulating centriole half-life during C. elegans oogenesis and embryogenesis
Approaches: RNAi-based functional genomic screen, live imaging, image analysis, molecular biology, cell biology.
Ideal for students in: Life Sciences, Bioengineering.
Keywords: C. elegans, centriole, organelle removal

Supervisor:  Pierre Gönczy   
Contact: [email protected]

Required: –

Posted in 2025 
Cell biology
Wet
Gönczy Analyzing centriole fate during zebrafish muscle formation

Objective: monitor centrosome and centriole fate during muscle formation in zebrafish embryos.
Approaches: 4D live imaging using light-sheet microscopy, injection of RNA/DNA into zebrafish embryos, develop and apply tracking algorithms to monitor centrosomes and centrioles.
Ideal for students in: Life Sciences, Bioengineering.
Keywords: Myogenesis, zebrafish, centriole, live imaging

Supervisor:  Pierre Gönczy   
Contact: [email protected]

Required: –

Posted in 2025 
Developmental biology
Dry and wet
Gönczy Single cell RNA-seq of centriole biogenesis in Naegleria

Objective: discover and characterize components induced during de novo centriole biogenesis in the protist Naegleria gruberi
Approaches: RNA-seq, cell biology, immunofluorescence, expansion microscopy, live imaging.
Ideal for students in: Life Sciences, Bioengineering.
Keywords: Naegleria gruberi, centriole, de novo biogenesis, RNA-seq

Supervisor:  Pierre Gönczy   
Contact: [email protected]

Required: –

Posted in 2025 
Cell biology
Dry and wet
Gönczy Evolutionary diversity and origin of centriolar proteins

Objective: identify homologues of fundamental centriolar proteins across the domains of life and thus help trace the origin of the centriole organelle.
Approaches: computational biology, structural prediction, cell biology
Ideal for students in: Computer Sciences, Life Sciences
Keywords: AlphaFold, Foldseek, phylogenomics, centriole

Supervisor:  Pierre Gönczy   
Contact: [email protected]

Required: –

Posted in 2025 
Computational Biology
Dry
Schoonjans Metabolic Regulation of Liver Cell Function and Stress Response

Join our research team to investigate how specific metabolites influence hepatic energy metabolism, oxidative stress resilience, and mitochondrial function. This project will provide hands-on experience with cutting-edge techniques in cellular metabolism and functional genomics.

Key Techniques & Approaches:
Metabolomics (LC-MS/MS, GC-MS) – Profile intracellular and extracellular metabolites to track metabolic fluxes.
Transcriptomics (RNA-seq, qPCR, RT-qPCR) – Analyze gene expression changes in metabolic and stress response pathways.
Mitochondrial Functional Assays (Seahorse XF, Oroboros O2k) – Assess mitochondrial respiration, ATP production, and metabolic flexibility.
Oxidative Stress and Redox Biology (ROS detection, GSH/GSSG assays, NADPH measurements) – Investigate how cells manage oxidative stress.
Advanced Cell Culture (Primary hepatocytes, 3D hepatic spheroids) – Study metabolic adaptations in physiologically relevant liver models.

What You’ll Gain:
Hands-on training in state-of-the-art bioanalytical techniques used in metabolic research.
Experience in multi-omics data integration to understand metabolic regulation.
The opportunity to contribute to a high-impact research project on liver metabolism.
Keywords: Liver metabolism, mitochondria, cell biology, omics

Supervisor:  Kristina Schoonjans
Co-supervisor: Hadrien Demagny   
Contact: [email protected]

Required: Basic knowledge/skills in biochemistry, molecular biology and programming

Posted in 2025 
Molecular biology
Dry and wet
Schoonjans Developing an Advanced Live Biotherapeutic Product for Intestinal and metabolic Health

This project aims to optimize a microbiome-based approach to enhance the production of secondary bile acids, known for their role in promoting intestinal stem cell-mediated regeneration and gut barrier integrity.
Methods:
-Culture anaerobic bacteria and establish a bile acid-modifying bacterial consortium.
-Assist in colonizing the mice with the selected bacterial strains.
-Evaluate the colonization efficiency using qPCR and bile acid profiling.
-Evaluate the therapeutic effects of the bacterial consortium through immunofluorescence stainings and transcriptomic analysis.

Expected Outcome:
Development of an advanced Live Biotherapeutic Product that could be used as a therapy to treat chronic gut disorders and obesity.
Keywords: Microbiome, bile acids, intestinal stem cells, gut barrier

Supervisor:  Kristina Schoonjans
Co-supervisor: Antoine Jalil   
Contact: [email protected]

Required: Basic knowledge/skills in biochemistry, molecular biology and bioinformatics

Posted in 2025 
Molecular biology
Dry and wet
Radenovic Simulation for nanoparticle gating on solid-state nanopore

To replicate gating functions in artificial solid-state nanofluidics implies significant understanding and applications for biosensing, drug delivery, and ionic-based computation such as neuromorphic computing, etc. In our lab, we have realized rapid and reproducible gating behaviors with nanoparticles (~30 nm in diameter) on the SiNx-based nanopores (sub-10 nm). To fully understand the gating mechanism in our system, and offer guidance for further optimization of the experimental performance, therefore, we are offering a master/semester project related to the COMSOL simulation of nanoparticle gating. The task of student will be to adapt the nanopore simulation model for our nanoparticle gating system, and to perform first numerical simulation in a stationary and symmetrical regime to understand how the nanoparticle affects nanopore conductance, and then develop non-symmetrical (with nanoparticle off-axis) simulation model to understand the gating performance.
Keywords: Nanopore, nanoparticle gating, simulation

Supervisor:  Aleksandra Radenovic   
Contact: [email protected]

Required: Basic knowledge of thermodynamics, electrostatics and fluidics; numerical simulation with Finite Element Method, ideally in COMSOL platform. –

Posted in 2025 
Bioengineering
Dry
Pardon Novel Design of Microfluidic Mixers for Lipid Nanoparticle (LNP) Synthesis

Lipid nanoparticles (LNPs) are emerging as essential vehicles for drug delivery, particularly in the fields of gene therapy, mRNA vaccines, and targeted therapeutics. Microfluidic technology presents a promising solution, but existing mixer designs have limitations in achieving LNP for targeted tissue-specific delivery application. This project aims to develop an innovative microfluidic mixer design for the synthesis of ligand-guided targeted Lipid Nanoparticles (tLNPs). By optimizing fluids mixing and testing novel materials, we seek to enhance the quality and consistency of tLNPs, crucial for targeted drug delivery of mRNA to the T cells. The research will involve prototype fabrication, mixing parameters optimization and experimental validation using in vitro cell culture. The project is an established collaboration between BET platform EPFL and Brain Tumor and Immune Cell Engineering lab UNIGE at AGORA research center.
Requirements:
-High motivation and interest in the topic
-Wet-lab experience, cell culture, FACS, transfection methods
-Knowledge of size-based molecule separation methods (SEC, ultrafiltration, dialysis)
-Knowledge of state-of-the-art human gene therapy is a plus
-Background in material sciences and particle characterization methods is a plus
-Practical experience in microfluidics for biological application is a plus

Application: To apply, please include a short motivation letter and your CV.
Keywords: Lipid nanoparticles, microfluidic, FACS, tLNPs

Supervisor:  Gaspard Pardon
Co-supervisor: Dzhangar Dzhumashev, UNIGE   
Contact: [email protected]

Required: BSc or MSc student

Posted in 2025 
Bioengineering
Dry and wet
De Los Rios Protein disaggregation by the bacterial Hsp70 system

In eukaryotes, protein disaggregation by the Hsp70 system is helped by proteins of the Hsp110 class (which is itself a subclass of the Hsp70 superfamily), along physics principles that are more and more understood. No such system has been identified in bacteria, although bacteria are known to be extremely effective at finding clever solutions.
In this project we want to explore if the bacterial HscC member of the Hsp70 family could play a similar role, and to quantify the role of GrpE, another partner of Hsp70, has in protein disaggregation.
Keywords: Protein disaggregation, Hsp70, bacterial cells

Supervisor:  Paolo De Los Rios
Co-supervisor: Mathieu Rebeaud   
Contact: [email protected]

Required: Enzyme assays, gel electrophoresis, photospectrometry, protein expression and purification

Posted in 2025 
Molecular biology
Wet
Lemaitre Drosophila immunity

The Drosophila antimicrobial response at the time of the Cas9/CRISPR gene targeting revolution
The application of Drosophila genetics has generated insights into insect immunity and uncovered general principles of animal host defense. These studies have shown that Drosophila has multiple defense “modules” that can be deployed in a coordinated response against distinct pathogens. Today, Drosophila can be considered as having one of the best-characterized host defense systems among the metazoan. Until recently, a detailed understanding of the fly immune response was hampered by the difficulty of generating loss-of-function mutations as well as the technological limits of the RNAi approach. The Cas9/CRISPR revolution offers new opportunities to revisit in a systematic manner Drosophila immunity. At the interface between large-scale genomic studies that lack resolution and individual gene analysis that lack breadth, our laboratory has undertaken a meso-scale ‘skilled’ analysis of immune modules, notably by addressing the individual and overlapping function of large immune gene family. The aim of the master project is to characterize the function of host defense modules (ex. antimicrobial peptides, phagocytosis,….) or specific genes in immunity using powerful genetic approaches and various model of infection. This master training will provide expertise in innate immunity, Drosophila genetics, molecular biology working at the organismal level.
Methods: Drosophila genetics, molecular biology, genomic, histology, microbiology, bioinformatic.
Keywords: Genetics, infection, signaling, immunology

Supervisor:  Bruno Lemaitre   
Contact: [email protected]

Required: A background in life science

Posted in 2025 
Infectious diseases
Wet
Lemaitre Psychology of science

Considering the impact of science on society, developing a better understanding of the social and psychological factors shaping the scientific community is important. Such a project requires bringing experts together, who truly understand academia and have experience in different fields, such as life sciences and social sciences. In this framework, we aim to explore three sets of psychological and sociological factors that are not usually associated with science that could however influence career success in academia. In particular, we aim to better understand how romantic relationships, personality, and thinking styles shape the scientific community and influence career progression in science. For this, we will use validated tools and measures to assess personality and cognitive traits and launch large-scale surveys as well as semi-structured interviews to gather and analyze data. By bringing together experts in social and psychological research with scientists working in experimental life science laboratories, we hope to generate a rich data set that will have a significant impact on the growing field of metascience. Our project has the potential to provide critical insights into implicit factors that drive the emergence and success of leaders, and thus has significant implications for the scientific community and beyond.
Keywords: Psychology, datascience, metascience

Supervisor:  Bruno Lemaitre   
Contact: [email protected]

Required: Expertise in datascience and programming

Posted in 2025 
Cell biology
Dry