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.
Due to the Coronavirus, the June 2021 Hiring Days will take place remotely. Program to follow.
The Fellay lab is a mostly computational group that applies large-scale genomic and bioinformatic approaches to explore the impact of genetic variation on immune parameters and host-pathogen interactions.
A PhD position is available starting in early 2021 to work on human genomics of viral diseases. More specifically, the project will include:
– Analyses of the transcriptomic responses to viral antigens and to type-I interferons to identify the genes and pathways involved in the human response to viral diseases
– Sequencing and bioinformatic analyses of exomes/genomes from patients with severe clinical presentations of common viral diseases, to identify, validate and functionally characterize the genetic factors involved in unusual susceptibility to infection.
Proteostasis and cell fate
The identity of a given cell is ultimately determined by its proteome content. While self-renewing stem cells or differentiated cells need to maintain their proteome over time, differentiating cells need to rapidly alter their protein content to change their identity. Whether these different requirements are related to differences in proteome turnover rates is not known. This project will tackle this question using differentiating pluripotent stem cells as a model system. You will learn to use broad range of molecular biology, biochemistry, cell biology, microscopy and genomics approaches, including cutting-edge technologies such as CRISPR-Cas9 knock-in, quantitative live cell fluorescence microscopy with automated tracking, and single cell RNA sequencing.
At least 2 openings are available for the April 15, 2021 deadline. 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.
In the Neuroengineering Laboratory, we are reverse-engineering transgenic flies, Drosophila melanogaster, to better understand the mind and to design more intelligent robots. Flies are an ideal model for reverse-engineering: they generate complex behaviors, their nervous systems are small, and they are genetically malleable. Our lab develops and combines microscopy, machine learning, genetics, and computational models to address systems-level questions.
We are always looking for talented researchers to join our team and actively also encourage applications from female scientists. Currently, we have PhD openings to study the following research areas:
(1) Uncover global neural dynamics associated with internal state changes resulting from social interactions, drug responses, sleep, and hunger.
(2) Genetically rewire animal behavior.
(3) Reveal neuronal population dynamics for adaptive limb control.
Join us! There is much to discover!
Evolutionary diversity and origin of centriolar proteins
Centrioles are small organelles that are critical for forming cilia, and which exhibit a striking 9-fold radial symmetric arrangement of microtubules, but whose evolutionary origin remains unclear. We discovered that the evolutionarily conserved SAS-6 proteins self-assemble into 9-fold radially symmetric structures thought to template the formation of the entire organelle.
Identify homologues of fundamental centriolar proteins such as SAS-6 across the domains of life, through protein sequence data analysis, including sequence covariation and structure prediction. Test newly identified candidates in cell free assays, including with chimeric proteins. Through the above approaches, help trace the origin of the centriole organelle.
Computational biology, structural prediction, cell biology
Collaboration between the Bitbol and Gönczy laboratories (EPFL, Life Sciences), as well as with the Dessimoz laboratory (UNIL and SIB).
Persat lab: p-lab.science
The lab is looking for a student interested in implementing interferrometric scattering microscopy for the visualization of bacterial extracellular filaments like flagella and pili (see Tala et al., Nature Microbiology 2019). The ideal candidate is a student interested in bioengineering or biophysical problems eager to implement new microscopy methodologies, or a microscopist interested exploring new frontiers of biophysics, all with applications to infectious diseases.
More generally, our lab investigates mechanical regulation of bacterial physiology and infection, in particular via mechanosensing. Our team is highly multidisciplinary, combining techniques from physics, engineering and biology.
We work at the intersection of physics and systems biology. We would like a new PhD student to join us who likes theory, computation, and experiments. The experiments involve yeast, which we manipulate genetically to break their DNA to analyze the dynamics of their checkpoints, to perform directed evolution using optogenetic controls, or to analyze instabilities in their genetic networks. (Exact project to be decided.) On the theoretical side, our interests extend from image analysis using neural networks, to data analysis and modeling, to proving theorems. Feel free to get in touch before or after your application.
Expanding the universe of protein functions by computational protein modeling and design for synthetic biology and biomedicine
Our lab is developing and applying novel hybrid computational/experimental approaches for engineering classes of proteins with new 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 for macromolecular modeling and design. Ultimately, we aim at developing a versatile tool to leverage the engineering of novel potent, selective therapeutic molecules and the de novo design of synthetic proteins, networks and pathways for reprogramming cellular functions.
Projects in the lab are often multidisciplinary and involve the development of novel methods (e.g. Feng et al., Nat Chem Biol 2016, Nat Chem Biol 2017) and their application involving experimental studies (e.g. Young et al., PNAS 2018; Chen et al., Nat Chem Biol 2020; Yin et al., Nature 2020). Projects involving internal collaborations between computational biologists, physicists and experimentalists in the lab are frequent. Specific research topics include the de novo design of allosteric protein biosensors, highly selective and potent mini-protein and peptide therapeutics, novel membrane receptors and signaling pathways reprogramming immune cell functions for improved cancer immunotherapies, and the development of novel algorithms for modeling & design of protein structures, interactions and motions.
Candidates should have strong programming skills in C/C++ and python. Some knowledge of bioinformatics, machine learning and/or computational biomolecular modeling are welcome.
Interrogate genome sequences with protein and systems-level modeling for precision personalized cancer medicine.
Our lab is developing and applying novel computational approaches to uncover the molecular and systems principles that regulate protein and cellular signaling. Using this understanding, we aim at predicting the effects of genetic variations on protein structure/function and cellular networks for personalized cancer medicine applications.
This specific project involves the analysis of genome sequences with protein and systems modeling approaches to predict the effects of genetic variations on protein and network structure/function for personalized cancer medicine applications. These studies will ultimately shed light on common mechanisms of cancer progression, and provide a rational basis for future personalized cancer diagnoses, risk stratifications and treatments.
Candidates should have a strong background in bioinformatics, data mining, machine learning, strong programming skills in C/C++ and python, and some knowledge of cell and structural biology.
Genetic and adaptive basis of evolution of brain size and cognition
Jaksic lab is recruiting a PhD student to work on genetic basis and evolution of cognitive ability and related neuronal traits.
Our lab’s mission is to merge the fields of experimental evolutionary biology and neuroscience by developing high-throughput integration of technologies from both fields with the end-result of experimentally evolving a cognitive brain in a model organism. We are specifically interested in exploring and mapping genetic variation underlying complex behavioral traits such as cognition in Drosophila using experimental evolution, next generation sequencing, high-throughput imaging techniques and complex behavior phenotyping. These technologies highly rely on successful integration of computational and quantitative approaches such as bioinformatics, machine learning (and other statistical approaches), automatization, and real-time image data analysis with experimental methods such as high-throughput phenotyping, robotics and efficient and creative experiment designs.
We are looking for highly motivated students with a good background in computational and quantitative skills (programming/scripting experience in languages such as Python, Matlab, Java, R or similar) and with a strong interest in animal behavior, evolution, genetics, or neuroscience.
The project you will be working on will heavily rely on your computational skills but is, in essence, highly multidisciplinary.
It will be based on design and automatization of high-throughput phenotyping of
– Various complex behavioral traits in a diverse genetic panel of Drosophila using automated real-time video tracking with implementation of machine-learning-based decision making and selection algorithms,
– Neuronal morphology in Drosophila using high-throughput imaging and image data analysis of fluorescently labeled neurons and other brain tissues,
and quantitative and computational analyses such as
– Genotype-phenotype mapping using whole-genome sequencing data,
– Generation and analysis of time-series, whole-genome sequencing and transcriptomics data.
The project, especially the experimental part, will be collaborative, and you will have assistance and guidance of other lab members. Additionally, through the design and development of automatized phenotyping algorithms you will have an opportunity to participate in the set-up of the first long-term evolution experiment for selection on cognitive ability. You will have a chance to generate and analyze time-resolved whole genome sequencing data that will enable us to observe and track real-time evolution of the brain from DNA to phenotype level.
Your position will be 50% experimental work (maintenance of long-term selection experiments, experimental setup for streamlined behavior data collection, neuronal tissue dissection and imaging) and 50% computational (whole genome transcriptomics and genomics analyses, behavior and image data analysis), however both will require creative and quantitative thinking, computational skills and interest in biology of behavior and evolutionary processes.
You can expect to develop and improve your bioinformatic and computational skills, but also learn population genetics, and quantitative techniques in evolutionary biology, gain knowledge of Drosophila genetics and neurobiology, and become an expert in experimental evolutionary neurobiology.
You can expect a supportive, inclusive, collaborative, dynamic and fun research environment, open-door mentorship, flat lab hierarchy, opportunity to attend international conferences, and access to the academic network of evolutionary biology.
You can learn more about the lab, projects and your future PI at jaksiclab.com.
If you think you would like to join our team and become a pioneer in experimental evolutionary neurobiology, do not hesitate to contact me!
Ana Marija Jaksic
Project title: Deciphering the cellular basis of limb regeneration
Aztekin lab is looking for colleagues!
Limb regeneration is one of nature’s biggest mysteries and has been mainly characterized at the tissue and genetic level. Our research aims to reveal the cellular basis of limb regeneration. We focus on individual cells, cell types, and cellular mechanisms. Our research characterizes epidermal cell types (e.g. ROCs, AER cells) that act as signalling centres critical for appendage regeneration. By secreting high levels of varying mitogenic, chemotactic, and inductive signals, these cell types can influence stem-cell-like and progenitor cells to build a lost appendage. In our lab, we aim at revealing features of these signalling centre cell types, and how they regulate dynamic processes for limb growth and patterning.
We study the limb regeneration potential of cells by following a comparative approach between regeneration-competent Xenopus laevis tadpoles, and regeneration-incompetent mice. Research in our lab harmonizes traditional developmental biology and embryology approaches with innovative imaging and single-cell multi-omics methods (e.g. scRNA-seq). Finally, to uncover mechanisms that are not feasible in vivo, we develop simplified ex vivo and in vitro systems, allowing the study of complex limb regeneration in a dish.
We are looking for colleagues that are fascinated by limb regeneration and want to develop and address outstanding questions in this field. Potential projects can be discussed with candidates based on their interest and background. Our projects use different molecular & cellular biology, developmental biology, or computational biology methods. We thrive on combining various fields, and colleagues who have a background in any of these fields but want to explore more are encouraged to apply. You can expect a supportive, fun, and dynamic research environment. Our lab also has a strict policy for a flat lab hierarchy and an open-door mentorship. If you are excited to explore the unknowns of limb regeneration using innovative approaches, please contact us.