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
At least 2 openings are available. PhD projects will be available in the following two areas of research:
- 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.
- 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.
Microbes form complex communities, or microbiomes, that sustain Earth systems, underpin the health of animals and plants and fuel biotechnological applications. My group uses environmental genomics to study the ecology of microbiomes at global scale. We are looking for two PhD students to tackle the following projects:
1 – How do ecological factors shape microbial immune strategies?
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.
The goals of this PhD project will be to combine computational biology and environmental genomics to (i) establish a census of antiviral defense systems throughout cultivated and uncultivated microbes, (ii) map the distribution of these defense systems across microbiomes (human, ocean, soil) using 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 test data-driven hypotheses and improve our mechanistic understanding of microbial immunity.
2 – How can we reconcile the ecology and evolution of microbial altruistic immunity?
Microbes have evolved hundreds of antiviral defense systems that make up their complex immune system. The majority of those systems appear to be altruistic: upon detection of the infection, the host kills itself. The evolutionary success of this immune strategy in spite of its ecological cost represents a fascinating biological conundrum that has yet to be explored in the context of microbial communities.
The goals of this PhD project will be to combine microbial ecology, evolution, and genomics to (i) identify the microbial traits associated with altruistic immunity, (ii) establish the ecological niches and population structures that support altruistic immunity, and (iii) study population-level synergies between altruistic and other immune strategies.
This work will improve our understanding of the eco-evolutionary conditions necessary for the emergence of altruistic immunity in microbial communities, which is likely to provide insights into the emergence of multicellular immunity and programmed cell death across domains of life. In addition, there will be opportunities to bridge computational and experimental work through collaborations at EPFL.
For any questions, please contact: [email protected]
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.
The amino-acid sequence of a protein encodes its function, including its structure and interactions. In evolution, random mutations affect the sequence, while natural selection acts at the level of function. Hence, shedding light on the sequence-function mapping of proteins is central to a systems-level understanding of cells. The current explosion of available sequence data enables data-driven approaches to this question. Meanwhile, understanding the evolution of protein families is also a fundamental question. Phylogeny reconstruction from sequence data has important applications, such as gaining insight in the early history of life, and developing new vaccines against pathogens.
In alignments of homologous protein sequences, which have significant similarity due to shared ancestry, correlations exist between certain amino-acid sites. These correlations can arise both from functional optimization, as homologs tend to maintain similar structures and functions, and from historical contingency. They can thus give us important insights into protein function and evolution. However, these correlations are traditionally ignored when reconstructing phylogenies. We would like to develop new methods that take into account these correlations. We also have interests in the evolution of specific proteins, in predicting interaction partners from protein sequences, and in making sense of collective modes of correlations in protein sequences. We employ both traditional inference methods and new machine learning approaches, such as protein language models.
Thesis: Computational analysis of single cell sequencing data to elucidate the mechanistic complexity of spinal-cord injury and movement disorders
Background
.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.
Project
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.
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.
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.
Antibodies are key components of protein- and cell-based therapies. One tool for antibody development that laboratories around the world are focusing on is artificial intelligence (AI), leveraging structural and binding data to create prototypes of proteins that bind their targets well. However, AI tools are currently far from generating good designs with high success rates. Further, ‘smart’ proteins that switch on or off are not within the scope of AIbased tools currently. More importantly, we also need breakthroughs in experimental techniques that make it easy (and cheap) to test and improve on designs. We want a PhD
student to join our lab, who is interested in developing both
1) novel experimental directed evolution techniques and
2) AI-based design methods
to make better binding, smarter, more controlled, and cheaper antibodies.
Contact Prof. Sahand Rahi ([email protected]) for more information.
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!”
https://www.epfl.ch/labs/salathelab
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 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 tumours (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 PhD position to offer:
Project 1: Deciphering the origin and somatic evolution of pediatric brain tumours.