EDCB Open positions

This page will be updated, as the EDCB program will be informed of new positions becoming available for the Hiring Days event at EPFL. Meanwhile, do not hesitate to contact the laboratories which interest you to find out whether they have upcoming openings for PhD students.

Next Deadline for applications : April 15, 2024


In the Neuroengineering Laboratory, we are reverse-engineering cognitive and motor behaviors in the fly, Drosophila melanogaster, to better understand the mind and to design more intelligent robots. Flies are an ideal model: they generate complex behaviors, their nervous systems are small, and they are genetically malleable. Our lab develops and leverages advanced microscopy, machine learning, genetics, and computational modeling approaches to address systems-level questions.

We are always looking for talented researchers to join our team. Join us! There is much to discover!”


At least 2 openings are available. PhD projects will be available in the following two areas of research:

  1. Biological nanopores for single-molecule sensing

Nanopore sensing is a powerful single-molecule approach currently developed for the precise detection of biomolecules, as for instance in DNA and protein sequencing. Our laboratory is developing this technology exploiting the properties of biological pores. Recently, we showed that aerolysin, a pore-forming toxin, exhibits high sensitivity for single-molecule detection and can be ad hoc engineered for different sensing tasks. The goal of this project is to develop and characterize aerolysin-based nanopores as sensing devices to be applied for genome sequencing, proteomic analysis and disease diagnosis. The project is highly interdisciplinary, includes experimental and computational aspects and interactions with a diverse network of collaborators.  Students with a background in biochemistry, physics, bioengineering and computational sciences are encouraged to apply.

  1. Integrative modeling at the membrane-protein interface

Molecular interfaces are essential for the formation and regulation of all assemblies that sustain life, to define cellular boundaries and intracellular organization, and to mediate communication with the outer environment. Our laboratory has been studying the molecular mechanisms governing the association of proteins to their membrane interfaces in order to understand the functional implications of this interplay. Multiple projects are available that focus on the theoretical and computational investigation of the structural and dynamic properties of membrane protein systems. All of them are addressed in synergy with experimental collaborators to allow for an efficient integration of biochemical and biophysical data. Students with a background in biochemistry, physics, bioengineering and computational sciences are encouraged to apply.

The Oates lab is exploring how spatio-temporal patterns emerge at the tissue level from noisy cellular and molecular interactions using a population of genetic oscillators in the zebrafish embryo termed the segmentation clock. This multi-cellular clock governs the rhythmic, sequential, and precise formation of embryonic body segments, termed somites, and exhibits a rich set of spatial and temporal phenomena spanning from molecular to tissue scales. Defects in this clock underlie human congenital mal-segmentation disorders (hereditary scoliosis).

Although the segmentation clock has been the dominant paradigm for 20 years, this model does not account for a fascinating classical result: the heat-shock echo, in which periodic segment defects recur, like an echo, along the axis. The interval separating the defects is 5 segments, but – critically – there are no known multiple-segment periodicities in the segmentation clock. This suggests that something fundamental is still missing from our overall picture of segmentation.

Using innovative microscopy techniques, transgenic zebrafish, biochemistry, mechanical manipulation, deep sequencing, physical modeling and good old-fashioned heat-shocks, we aim to discover the mechanism underlying the repeated defects. We will characterize and investigate phenomena during the defects recently observed in our lab at multiple scales: single cell, synchronization between neighbor cells, large-scale wave patterns and mechanics of the tissue. We will also search in an unbiased way for genes that predict the echoes. If these sound to you like interesting questions and approaches to be explored in a challenging and interdisciplinary PhD, please apply to the program and contact the Oates lab via [email protected].


The physical properties of the mitochondrial matrix. ERC-funded project. The current dogma is that the mitochondrial interior, or matrix, behaves as a viscous fluid, albeit one with a complex shape. Interestingly, it has been reported that in vitro, different respiratory states of mitochondria correlate with differences in mitochondrial matrix viscosity, ultrastructure, and density. Fluorescence-based ratiometric, anisotropy, and recovery methods have been applied to measure its viscosity, but with results varying over two orders of magnitude. Intriguingly, motility and internal structure have been linked to metabolic states. More recently, it was reported that the internal ‘temperature’ of mitochondria is adaptive, and reaches nearly 50 °C when they are metabolically active. The field missing a comprehensive study that considers the mitochondrial matrix as a responsive complex fluid with potential for complex or non-equilibrium state behavior, the goal of this project.


In the past few years, studies have begun to demonstrate the potential of personalized nutrition in controlled settings, but their generalizability to the broader population remains an open question. The Digital Epidemiology Lab is harnessing the power of the digital health approach to understand the link between dietary patterns and blood glucose response, and its mediation through the gut microbiota. Based on a novel, very large data set from the Swiss “Food & You” digital cohort, we are training algorithms to create personalized diets with the goal to lower postprandial glucose response (PPGR). In the future, we are planing intervention studies to test the validity and scalability of this approach.

The source of the data, the Food & You cohort study, is a globally unique “digital cohort” on personalized nutrition in Switzerland, developed and run by the lab. As of March 2023, the cohort has collected detailed behavioral and biological data from over 1000 people within Switzerland, and has started data collection in Germany. The data comprises detailed, high temporal resolution nutritional data (at least 14 consecutive days of detailed food tracking data with images), physical activity data, gut microbiota data, and data from continuous blood glucose sensors with over 1.5 million recordings, all collected simultaneously, in addition to demographic data. The study has created an unparalleled dietary dataset of over 300,000 human-validated dishes with a total of over 42 million kcal.

We are now interested to use this data to develop recommender systems, and to investigate various aspects about the interactions of the multimodal data collected in the Food & You study. We are also planning Food & You 2, a new, extended version of the Food & You cohort. We are looking for a talented and motivated PhD student to participate in this project on data-driven personalized nutrition.


The EPFL Digital Epidemiology Lab, partnering with Nestlé Research, has an opportunity for a PhD project on machine learning for food image recognition for dietary intake capture. Dietary intake capture is notoriously challenging and can be made easier and more accurate by allowing participants to take pictures of what they eat and then using computer vision algorithms to recognize the foods. The pipeline for extracting nutritional information from images has multiple steps, including segmentation, classification, and portion estimation. All these steps are made challenging by the peculiarities of food images and limited availability of training data. With the rise of foundational models in artificial intelligence, there is further potential to revolutionize the way we approach these challenges, harnessing their capability to excel at automated dietary intake capture with limited training examples.

We are looking for a talented and motivated PhD student with a background in computational biology or data science to participate in this project and explore the possibilities that foundational models bring to the field of dietary intake capture.


The primary objective of this interdisciplinary PhD project is to investigate the brain lipidome as a key to understanding the complex interplay between lipid distribution, lipid dysmetabolism, and alpha-synuclein (aSyn) pathology formation in Parkinson’s disease (PD) using advanced computational approaches and cutting-edge technologies. By exploring the role of regional brain lipid alterations in the development and progression of aSyn pathology formation and neurodegeneration, this project offers a unique opportunity for a PhD candidate who is deeply interested in developing computational models and excited by the rapidly evolving technologies in the field.

Leveraging cutting-edge technologies such as matrix-assisted laser desorption/ionisation (MALDI) mass spectrometry imaging (MSI), this project aims to map the lipidome in the entiremouse brain in 3D and at micrometric resolution. The main activities of the PhD candidate will focus on Specific Aim 1 and Specific Aim 2:

  1. Determining the role of specific lipid configurations in regulating α-synuclein pathology formation and spreading in the α-synuclein pre-formed fibrils (PFF) model of PD.
  2. Investigating the impact of de novo α-synuclein aggregation and seeded α-synuclein aggregate formation and propatation on regional lipid metabolism.

As part of a vibrant research team, the PhD candidate will collaborate with experts in the fields of computational biology, lipid cell biology, neurodevelopmental systems biology, and chemical biology of neurodegeneration. This project provides an excellent opportunity to develop advanced computational skills, work with large-scale datasets, and employ innovative analytical pipelines to address critical questions in PD pathology.
The interdisciplinary nature of this project will allow the PhD candidate to acquire valuable expertise in both neuroscience and computational biology, positioning them at the forefront of cutting-edge research in neurodegeneration.
This project is flexible to a certain extent, allowing the candidate to decide whether to also extend their research to experimental parts, depending on their interests and expertise.

The Living patterns lab at EPFL IPHYS has three open PhD positions. We are an experimental
Biophysics group exploring a wide variety of imterdisciplinary problems at the interface
of cell and developmental biology and active matter physics. Research in the lab
is centered around understanding patterning and flow generation in multiciliated cells. Individual
projects are shaped by student’s interests, for more information visit our lab website
http://livingpatterns.group or email us directly [email protected].

The Laboratory of Computational Neuro-Oncology at the École Polytechnique Fédérale de Lausanne (EPFL) focuses on biomedical data science for children and young adults with brain tumors (waszaklab.org). The group studies clinical cancer genomes and develops computational and experimental methods to advance diagnostics that are globally accessible and transformative for brain tumour patients. We have the following four PhD positions to offer:

Project 1: Deciphering the cellular origin of brain tumours at single-cell resolution
Project 2: Digital neuropathology 2.0: integration of subcellular, high-plex in situ data with histopathology
Project 3: Leveraging long-read sequencing in pediatric neuro-oncology
Project 4: Targeting oncogenic enhancers in pediatric diffuse gliomas

Barth Lab PhD project
Expanding the universe of protein functions for synthetic biology and biomedicine
Our lab is developing and applying hybrid AI-based computational/experimental approaches for engineering classes of proteins with novel functions for cell engineering, synthetic biology and therapeutic applications. Through our bottom up design approach, we also strive to better understand the molecular and physical principles that underlie the emergence, evolution and robustness of the complex functions encoded by proteins and their associated networks.
We are part of RosettaCommons (https://rosettacommons.org/), a collaborative network of academic laboratories that develop the software platform Rosetta and AI-based approaches for macromolecular modeling and design. Ultimately, we aim to develop a versatile tool for designing novel potent, selective therapeutic molecules, synthetic proteins, receptor biosensors, networks and pathways for reprogramming cellular functions. We are also affiliated to the Ludwig Institute for Cancer Research in Lausanne.
Projects in the lab are often multidisciplinary and involve the development of novel methods (e.g. Feng, Nat Chem Biol 2016; Nat Chem Biol 2017; Paradis, Nat Comm 2022; Dumas, biorxiv 2023) and their application involving experimental studies (e.g. Young, PNAS 2018; Chen, Nat Chem Biol 2020; Yin, Nature 2020; Keri et al., biorxiv 2023; Jefferson, Nat Comm 2023). Projects involving external collaborations with other research groups around the world or internal collaborations between computational biologists,
physicists and experimentalists in the lab are frequent. We also actively translate our findings to the clinic in collaboration with physicians (e.g. Dr. Arber, Coukos from the Ludwig Institute for Cancer Research). Specific research topics include: 1. The design of protein biosensors, mechanosensors and signaling receptors for reprogramming cell (e.g. CAR T cell) functions and enhance cell-based therapies; 2. The design of highly selective and potent protein and peptide-based therapeutics towards challenging targets such as GPCRs or ion channels; 3. The study, prediction and design of protein dynamics and
allostery using AI and classic computational approaches; 4. The development of novel AI-based algorithms for modeling & design of protein structures, interactions and motions. Dry lab candidates should have strong programming skills in python/C/C++ and expertise in the development of deep learning methods. Knowledge in structural biology, bioinformatics, computational biomolecular modeling including molecular dynamics simulations is a plus. Candidates more oriented towards the wet lab should have strong skills in molecular and cell biology including experience in protein biochemistry,
mammalian cell culture, microscopy, and structural biology. Hybrid computational / experimental projects are also possible.

In Kim’s Lab in the Institute of Mechanical Engineering at EPFL, we are dedicated to acquiring fundamental insights on biological/living systems and soft/active materials, spanning from inert soft materials of foams, emulsions, colloidal systems to granular materials and active living materials of embryonic tissues, in-vitro cell culture systems and bacteria aggregates. By employing principles in mechanics and physics, we strive to develop novel theoretical framework to understand emergent structures, dynamics, and mechanical properties within these systems.

We are seeking talented, enthusiastic, and motivated students and postdocs to join our lab as founding members. We have two available positions: one postdoctoral fellow and one PhD student, or alternatively two positions for PhD students. The anticipated starting date for these positions is Autumn 2023, but there is some flexibility for negotiation.   

What We Do

We endeavor to study problems broadly in the field of biomechanics, biophysics, and soft condensed matter physics. Our work encompasses a wide range of research questions, including but not limited to the following:

  • Embryonic development
  • tissue morphogenesis
  • Structures and mechanics of soft materials
  • Inherent structures of amorphous materials
  • Non-equilibrium dynamics of active matter

The individual projects for students and postdocs will be jointly determined, taking into account the candidate’s expertise and specific interests.

Whom We Are Looking for

We are looking to build a diverse and collaborative group, and welcome applications from candidates with different backgrounds. Preferred skills and qualifications for successful candidates include:

  •  For PhD students: Bachelor’s and Master’s Degree in Mechanical Engineering, Materials Engineering, Applied Mathematics, Physics, Applied Physics, or Biology for PhD students
  • For postdoctoral fellow: PhD Degree in equivalent fields
  • Demonstrated excellence and previous research experience in analytic modeling, numerical methods, or in-vitro cell culture experiments
  • Excellent communication skills in English (both written and spoken)
  • A self-driven individual with an open mind and a willingness to explore new fields

What We Offer

  •  Highly competitive salary commensurate with previous experience, accompanied by comprehensive social benefits
  • Access to state-of-the-art computation and experimental facilities, enabling cutting-edge research opportunities
  • Prospects for participating in collaborations within multidisciplinary projects

How to Apply

Interested candidates are requested to prepare their application as a single pdf file, including the following documents:

  • A cover letter (maximum 1 page) describing research interests and demonstrating how your background and previous experiences align with the direction of our group
  • A comprehensive CV providing detailed information about your academic and professional background and skills, accompanied by contact information of three references.

The application should be direct submitted to Professor Sangwoo Kim ([email protected]) with the subject line, “Name: Application to PhD (or postdoc) Position”. For additional information regarding the position, please feel free to reach out to Professor Sangwoo Kim.

1 . Computational approaches to infections and antimicrobial resistance

We have one position for candidates with backgrounds in computational biology or data science. The Persat lab investigates how mechanics mediate the interactions between bacterial pathogens, microbiota and their hosts. We develop new engineering organoid-based models allowing us to study infections at high spatial, temporal and biological resolution. We recently developed scRNA-seq pipelines to acutely resolve the molecular mechanisms involved in interactions between host and microbes. By discovering new ways to interfere with infectious processes, this new approach allows us to design new therapeutic strategies to combat antibiotic resistant pathogens .

The project is focused on analysis of scRNAseq and RNAseq data of organoids and bacteria, along with microscopy image analysis during infection and colonization. This project is in collaboration with the Brbic lab at EPFL, specialized in the development of ML methods for the analysis of single cell datasets (https://brbiclab.epfl.ch/). 

2. Imaging infections and integration of transcriptomics data

We have rencetly developped novel imtaing methods to study bacterial infection at high resolution. For example, we visualize the formation of bacterial biofilms within human gut and lung organoids at the single cell and bacterium level. We are looking for a image analysis specialist with background in machine learning to implement U-Net and other ML approaches in analyzing these complex imaging datasets. In addition, we are looking into integrating transcriptomic data with  microscopy image data to identify phenotypic states involved during infections. The project may include experimental components to generate new datasets of developing biofilms and organoids.

Thesis: Computational analysis of single cell sequencing data to elucidate the mechanistic complexity of spinal-cord injury and movement disorders
.NeuroRestore is a laboratory of EPFL that develops and applies medical therapies aimed to restore neurological functions. We integrate neurotechnologies with innovative treatments developed through rigorous preclinical and clinical studies. These developments have led to breakthroughs for the treatment of paraplegia, tetraplegia and Parkinson’s disease. By working with our network of vibrant high-tech start-ups and established medical technology companies, we are committed to validate our medical therapy concepts and see them used every day in rehabilitation clinics worldwide.
Spinal cord injury (SCI) triggers a cascade of molecular and cellular responses that has yet to fully understood. The incomplete appreciation of the molecular tapestry underlying SCI has hindered the development of targeted therapeutic strategies that seek to alleviate symptoms and enable neuro-regeneration after injury. Here, we aim to leverage on revolutionary single-cell technologies to unravel the enigma of SCI at a molecular level and translate these findings into comprehensive SCI treatments.
This creates an opportunity for an interdisciplinary PhD project that interrogate spinal cord injury and related movement disorders across multi-omic single-cell modalities (snRNA-seq, multi-omic scRNA-seq + snATAC-seq, spatial transcriptomics) and combine bioinformatics tools, statistical methodologies and machine learning strategies. The project is divided into two general parts: (i) developing sophisticated frameworks of analyzing single-cell sequencing data and (ii), applying known and in-house tools to generate biologically meaningful insights into SCI and other diseases.
The PhD candidate should be extremely passionate about computational biology/bioinformatics, understanding the mechanistic complexity of the spinal cord and central nervous system, and developing precision medicine/therapeutics for SCI and other movement disorders.

Please contact [email protected][email protected] and [email protected] for more information.

Microbes form complex communities that sustain Earth systems, underpin the health of animals and plants and fuel biotechnological applications. These communities, or microbiomes, are shaped by microbe–virus interactions, resulting in an intricate microbial immune system that has not been explored in natural microbiomes. Yet, to understand the impact of microbe–virus interactions on microbiome function as well as microbiome- or phage-based applications, it is crucial to study the ecology of microbial immunity at the scale of microbiomes. We are looking for a PhD candidate to study the ecological structures of microbial immunity.

This PhD project will combine computational biology and environmental genomics to address the following questions: What are the ecological drivers that structure microbial immunity within and between microbiomes? What are the synergies between antiviral defenses that co-occur and co-express across microbial communities?

We will seek to (i) establish a census of antiviral defense systems throughout cultivated and uncultivated microbes using bioinformatic analyses of tens of thousands of genomes, (ii) map the distribution of these defense systems across microbiomes (human, ocean, soil) by leveraging metagenomics and metatranscriptomics (i.e., the direct sequencing of microbial DNA/RNA from microbial communities), and (iii) build ecological networks to unravel community-level interactions between defense systems.

This work will establish the first ecological map of microbial immunity, shedding light on its ecological drivers and laying the groundwork to understand the interplay between microbial ecology and immunity. In addition, there will be opportunities to bridge computational and experimental microbial ecology through collaborations to validate data-driven hypotheses and improve our mechanistic understanding of microbial immunity.

Our lab uses computational approaches to understand inter-individual differences in immune response and susceptibility to infection. 

We are looking for a PhD student to work on the genomic determinants of immunity and/or specific infectious diseases with a focus on AI-based approaches. The precise project will be defined based on ongoing research projects in the laboratory and student’s interests. 

Candidates should have a background in computer sciences or bioinformatics with a strong interest in applications of machine learning to biomedicine. Programming skills in Python and/or R are required. Knowledge of immunology or infectious diseases is a plus.