This page will be updated starting in the Spring of 2020, as the EDBB program will be informed of new positions becoming available for the June 2020 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 January 2021 Hiring Days will likely take place remotely. More information 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.
At least 2 openings are available for the November 1st, 2020 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.
We use the fly, Drosophila melanogaster, microscopy, machine vision, genetics, and computational models to identify how biological neural circuits control behavior. We aim to better understand the mind and to build more versatile robotic controllers. We use flies because they produce complex behaviors, have small nervous systems with stereotyped connectivity, and are genetically tractable.
We are currently excited to welcome additional PhD students to perform and/or analyze 2-photon optical recordings of neuronal population dynamics governing action selection and limb control. These measurements will be used to inform simulation and robotics work in the laboratory.
- Investigation of centriole number control mechanisms in human cells
Centrioles are small organelles that are critical for forming cilia, and which exhibit a striking 9-fold radial symmetric arrangement of microtubules. In proliferating cells, centrioles assemble once per cell cycle next to an existing centriole, reaching a length of ~500 nm. Three proteins are critical for the onset of centriole assembly in human cells: the kinase Plk4, as well as the coiled-coil containing proteins HsSAS-6 and STIL. Depletion of either component prevents centriole assembly, whereas their overexpression results in excess centrioles.
Combine experiments –depleting proteins or providing them in excess- and mathematical modeling to investigate how Plk4, HsSAS-6 and STIL ensure that a single centriole assembles next to each existing centriole. Investigate also mechanisms by which these components ensure that centrioles achieve a characteristic length.
Molecular biology, including CRISPR/Cas9 genome engineering, cell biology, live cell imaging, Fluorescence Correlation Spectroscopy (FCS), super-resolution microscopy (STORM, iSIM, Ux-EM-STED), mathematical modeling.
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).
Group website: https://www.epfl.ch/labs/bitbol-lab/
(1) Understanding how optimization and phylogeny shape protein sequences
Proteins play crucial roles in our cells. The amino-acid sequence of a protein encodes its function, including its structure and its possible interactions. In evolution, random mutations affect the sequence, while natural selection acts at the level of function. Shedding light on the sequence-function mapping of proteins is central to a systems-level understanding of cells, and has far-reaching applications in synthetic biology and drug targeting. The current explosion of available sequence data enables data-driven approaches to discover the principles of protein operation.
In alignments of homologous protein sequences, correlations exist between certain amino-acid sites. We aim to establish a full decomposition of these correlations, dissecting signatures from functional optimization, and from evolutionary history. We also aim to make new predictions for protein-protein interactions from sequence data, and to understand whether real proteins are mechanically optimized.
Several directions are possible, depending on the background and tastes of the student:
– Theoretical directions involve developing statistical physics based simulations to generate controlled synthetic data, and employing analytical calculations to make sense of the synthetic data.
-Data-driven directions involve analyzing real protein sequence data, as well as protein structure data.
(2) Characterizing the exploration of rugged fitness landscapes by subdivided populations
Populations of living organisms are pushed toward optimality by natural selection. However, from a given state, populations may not be able to reach the absolute maximum of a fitness landscape, which represents fitness (reproduction rate) versus genotype (genetic type). Indeed, fitness landscapes are often rugged, like the energy landscapes of glassy physical systems. Real microbial populations are subdivided between habitats, e.g. among different organs and among different hosts in the case of an infection. This may help microbial populations to better explore fitness landscapes.
We aim to characterize how subdivided microbial populations explore rugged fitness landscapes, and to establish a universal description of subdivided populations on graphs. We also aim to study the impact of population subdivision on the evolution of antimicrobial resistance, and to investigate how expanding populations explore rugged fitness landscapes.
Several directions are possible. They all involve a combination of analytical calculations and numerical simulations, with proportions depending on the background and tastes of the applicant:
– Studying biased random walks on rugged fitness landscapes and models of populations on graphs.
– Developing applications to antimicrobial resistance evolution and expanding populations.
In Oricchio lab, we combine experimental and computational expertise to understand the effects of genomic alterations in cancer development and progression.
A PhD student position is available to work on a project in which we are investigating how
cancer heterogeneity influences resistance to cancer therapies. In this project, we want to track cancer cell evolution
in response to specific treatments using single cell analyses coupled with genetic barcoding.
We will use computational approaches and mathematical modelling to anticipate mechanisms of resistance and predicting tumor evolutionary trajectories from the initial composition of the cancer cell population.
Students with interest in cancer genomics and a background in computer science, physics, bioengineering, computational biology are encouraged to apply.
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.