EDCB Open positions

https://www.epfl.ch/labs/ramdya-lab/

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!”

I actually have 2 different projects:

A Foundation Model and Knowledge System for Neurodevelopmental Biology Research

This PhD project addresses the growing challenge in neurodevelopmental biology where comprehensive molecular atlases remain largely inaccessible to researchers for routine experimental interpretation and hypothesis generation. Despite containing unprecedented cellular and molecular information, current atlases function as static databases that require specialized expertise to query effectively, limiting their practical utility for everyday laboratory research.

The project will develop a transformative AI system combining two innovative components: a cell-type-informed 3D foundation model that integrates spatial distributions with molecular profiles using dual-stream neural architecture, and NeuroBioKG, a literature-powered knowledge system that will mine decades of neurodevelopmental research to construct comprehensive knowledge graphs. The foundation model will be trained on millions of synthetic examples to enable complex tasks including gene expression prediction and experimental data augmentation through natural language queries, while the knowledge system will use graph retrieval augmented generation to provide contextual interpretation against established developmental principles.

Together, these components will transform molecular atlases from static resources into interactive research tools that actively support hypothesis generation and experimental design. The project is ideal for students with computational biology backgrounds within combining machine learning expertise with neurodevelopmental biology to create next-generation research AIs reaching expert-level insights.

Investigating Lipid Dysregulation in Neural Tube Defects and Metabolic Intervention Strategies

This PhD project addresses a critical gap in understanding how environmental teratogens disrupt neural tube development through lipid metabolism perturbations, and explores whether metabolic interventions can prevent or alleviate neural tube defects (NTDs). Despite growing evidence that teratogenic compounds affect lipid homeostasis during early brain development, the specific mechanisms linking lipid dysregulation to NTDs remain poorly characterized, limiting our ability to develop protective strategies.

The project will systematically investigate how teratogenic compounds induce lipid metabolic disruptions using state-of-the-art ex utero mouse embryo culture systems combined with spatial lipidomics and transcriptomics. You will expose developing embryos to well-characterized teratogens (including retinoic acid, methotrexate, and cyclopamine) and endocrine-disrupting chemicals, then use MALDI mass spectrometry imaging and spatial gene expression analysis to map region-specific changes in lipid composition and neural patterning. A key innovation will be testing whether metabolic modulators can rescue teratogen-induced defects by rebalancing disrupted lipid pathways.

The work will combine cutting-edge spatial multi-omics technologies with advanced computational modeling to identify metabolic intervention targets. Using machine learning approaches and biochemically-constrained models, you will predict which metabolic pathways could be therapeutically targeted to counteract specific teratogenic effects.

This project is ideal for students with backgrounds in developmental biology or biochemistry who are interested in combining experimental embryology with computational approaches to address clinically-relevant questions about birth defects and environmental health.

https://www.epfl.ch/labs/barth-lab/

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; Sengar, NeurIPS
2025) and their application involving experimental studies (e.g. Chen, Nat Chem Biol 2020; Yin,
Nature 2020; Jefferson, Nat Comm 2023; Chen, Nat Chemistry 2025). 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 (Dr. Arber, Ludwig Institute for
Cancer Research, see Rath, Nat Biomed Eng 2025). 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.

https://www.epfl.ch/labs/lpbs/

Intelligent Proteins and High-Performance Cells

How can proteins be engineered to make decisions, and how can cells be pushed beyond their natural performance limits? In the Rahi lab (LPBS), we study and design the dynamic computations of life. Using a combination of optogenetics, continuous directed evolution, and AI-driven protein design, we aim to create intelligent proteins, that is, molecules that integrate multiple inputs, switch states, and perform logic-like operations. At the same time, we use light-controlled evolution to sculpt high-performance cells, probing the physical and evolutionary limits of growth, variability, and robustness.

As a PhD student in our group, you will:

• Use AI-based design and simulations to generate and test candidate proteins that perform computational tasks at the molecular level.
• Develop and apply optogenetic and light-directed evolution platforms to re-engineer switchable proteins with novel functionalities.
• Explore how cellular physiology can be tuned and optimized through laboratory evolution, uncovering fundamental constraints on growth and decision-making.
• Work at the interface of machine learning, synthetic biology, biophysics, in an environment that bridges physics, biology, and engineering.

We are looking for highly motivated candidates from physics, bioengineering, and quantitative biology who are excited to combine computation and wet-lab experiments to solve fundamental and applied questions in synthetic biology.

Join us in building the next generation of programmable proteins and optimized cells, and help define what it means for biology to compute.

Contact: Prof. Sahand Rahi ([email protected])

https://www.epfl.ch/labs/mesobio/

We are excited to invite a passionate and motivated PhD student to join our lab on theoretical and
computational modeling of multicellular biological tissues under complex environments.
Specifically, the project will focus on understanding non-equilibrium dynamics and rigidity transitions
arising from distinct cellular and subcellular mechanisms. The Mechanics of Soft and Biological
Matter Laboratory (MESOBIO) at the Institute of Mechanical Engineering, EPFL, focuses on gaining a
fundamental understanding of biological and living systems as well as soft and active materials. We have
an open position for a PhD student in theory and simulations of biological tissues using the Active Foam
model, with implications for advancing the fundamental understanding of key biological processes such
as embryonic development and tissue morphogenesis. Applications will be reviewed until the position is
filled.

We welcome applications from candidates with different backgrounds. Preferred skills and qualifications
for successful candidates include:

• Bachelor’s and/or Master’s degree in Physics, Mechanics, Mechanical Engineering, or Materials
Science and Engineering, Bioengineering, Biology, Biophysics.
• Proven research experience in discrete element modeling of living or synthetic systems.
• Strong background in statistical physics, soft condensed matter physics, and continuum
mechanics, and numerical methods.
• Prior experience with high-performance computing facility.
• Excellent communication skills in English, both written and spoken.
• Self-driven and open minded with a strong willingness to explore interdisciplinary research.

We offer a highly competitive salary commensurate with previous experience, accompanied by
comprehensive social benefits. All students will have access to state-of-the-art computation and
experimental facilities, enabling cutting-edge research opportunities, and they will also have the
opportunity to participate in collaborations within multidisciplinary projects.

Interested candidates are requested to prepare their application as a single PDF file, including a cover
letter (maximum 1 page), describing research interests and demonstrating how your background and
previous experiences align with the direction of our group, and a comprehensive CV, providing detailed
information about your academic and professional background and skills, accompanied by contact
information for three references. The application should be directly submitted to Professor Sangwoo Kim
([email protected]) with the subject line, “Name: Application for PhD Position”. For additional
information regarding the position, please feel free to reach out to Professor Sangwoo Kim.

https://www.epfl.ch/labs/bitbol-lab/

We are studying biological evolution, from proteins to microbial populations, using theoretical modeling and computational data-driven approaches. In particular, at the scale of proteins, we are interested in developing inference methods to capture and predict protein function and protein-protein interactions. We are also aiming to decipher the constraints at play in evolution, and to understand and infer the evolution of proteins. For this, we develop and use machine learning models and statistical inference methods that start from biological data, typically protein sequence data, such as protein language models. Different projects are possible, ranging from methodology development to applications to specific proteins in collaboration with other groups. 

https://www.epfl.ch/labs/bitbol-lab/

https://www.epfl.ch/labs/gonczy-lab/

Uncovering evolutionary history and interactome of centriolar proteins

Centrioles are ancient eukaryotic organelles critical for cell division, motility and signaling. In this joint project between the groups of Prof. Bitbol and Prof. Gönczy, the evolutionary history of centriolar proteins will be uncovered using novel structure-aware phylogeny inference algorithms. Moreover, protein and proteome language models will be developed and deployed to infer the interactome of centriolar proteins, and investigate its evolution. Furthermore, building blocks of the centriole will be sought outside eukaryotes, thereby potentially uncovering the evolutionary origin of this critical organelle. 

https://www.epfl.ch/labs/lpl/

We are seeking outstanding and motivated PhD students to join our interdisciplinary group exploring how biological pattern and function emerge from molecular and physical interactions. PhD projects are available in the following areas:

Extreme Cellular Mechanics:

Extreme cell shape changes observed in free-living protists are among the fastest and most dramatic motions known in living systems. These rapid deformations are driven by centrin assemblies, yet, unlike other cytoskeletal proteins, the mechanisms underlying the assembly and force generation of these networks remain poorly understood. We are looking for students interested in uncovering the molecular and biophysical principles of centrin network formation, investigating how this cytoskeletal system organizes into filaments and networks capable of generating the forces that drive extreme cellular shape changes.

Behavioral Responses Enabled by Centrin:

The survival of free-living unicellular organisms depends on their ability to mount appropriate behavioral responses to environmental changes. We are looking for students interested in investigating how centrin networks encode and regulate these behavioral programs, thereby uncovering the molecular and mechanical basis of adaptive behavior in free-living eukaryotic cells.

Flow Generation by Cilia Arrays:

From unicellular swimmers to human airways, biological flows are generated by the collective motion of cilia. In most organisms, cilia form dense arrays of thousands of filaments that are highly patterned both spatially and temporally. We are looking for students interested in exploring how cilia patterning, geometry, and coordination determine flow generation, uncovering the biophysical principles underlying biological fluid transport.

The successful candidates will join a collaborative and stimulating research environment that bridges cell biology, engineering, and soft matter physics. Our projects offer opportunities to develop and apply advanced imaging and biophysical techniques, computational modeling, and theoretical frameworks to address key questions in active matter and cellular biophysics.

We welcome applicants from diverse backgrounds, including physics, biophysics, biochemistry, cell biology, and bioengineering. Candidates should demonstrate curiosity, creativity, and a strong interest in interdisciplinary research.

https://orcid.org/0000-0003-1572-2279

How did bacterial communication systems emerge and diversify? How can new systems evolve? How can we (re)engineer bacterial communication into novel functions?

In the Systems and Synthetic Evolutionary Biology Lab, we combine experimental evolution, high-throughput phenotyping, computational modeling, and evolutionary theory to uncover the principles behind the diversification of bacterial communication and harness them for new applications. As a PhD student in our group, you will:

• Reconstruct and experimentally test ancestral bacterial communication systems to uncover how they originated and diversified
• Map large-scale sequence–function relationships of bacterial communication components
• Apply biophysical and AI-based approaches to predict and design new communication circuits
• Explore how protein promiscuity, specificity, and modularity shape bacterial signaling and regulatory networks.
• Work in a highly interdisciplinary, collaborative, and international environment.

  We are looking for curious and motivated candidates from molecular biology, bioengineering, evolutionary biology, or related fields who are excited to combine wet-lab and computational approaches to tackle fundamental and applied questions. Join us in decoding and redesigning the molecular languages of bacteria!

  Contact: Dr. Cauã Westmann ([email protected])