EDCB Open Positions

This page will be updated starting in the Fall of 2019, as the EDBB program will be informed of new positions becoming available for the January 22-24, 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.

We have at least two possible projects for PhD students:

1) Biological systems are often said to be ‘robust’. This means that when they are perturbed, for example, by changing environmental conditions, they do not just stop working and break down. However, robustness certainly has limits, and we would like to explore those, because when systems are no longer robust, they are easier to manipulate. Robustness may be good biologically but it can be bad for us wanting to modify the system. Also, in some diseases, genes that were needed for robustness may be corrupted and we may be able to specifically target such cells using their non-robustness, which we call fragility. This project involves experiments involving yeast molecular biology, microcopy, convolutional neural networks, as well as theory to develop these ideas.

2) A goal of neurosciene is to predict an animal’s behavior by understanding its brain. However, for almost no animal can we actually do that well. Suppose we take one of the simplest brains studied in the lab (belonging to the roundworm C. elegans), we stimulate it as we want experimentally, and at the same time look at most of its neurons, as well as measure its behavior. Can you come up with theory and experiments to predict what the worm will do a few seconds later? This research involves C. elegans molecular biology, microscopy, microfluidics, convolutional neural networks, and theory.

The Suter lab is interested in quantitative analysis of gene expression to understand how cell identities are established and maintained. The PhD project we propose aims at quantitative, biophysical characterization of the transcription factor network that controls the identity of embryonic stem cells. It will involve cutting edge approaches such as genome editing, quantitative live cell imaging and cell tracking, and single molecule imaging. This project is part of a Sinergia Consortium and will involve interdisciplinary collaboration with our partner labs experts in microfluidics and in vitro transcription factor characterization (Maerkl lab, EPFL), and computational modelling of biological networks (van Nimwegen lab, University of Basel). 

We also propose a project to study the role of mitotic bookmarking in cancer stem cell self-renewal. Cancer stem cells are central to the fueling of tumorigenesis through their ability to self-renew. Over the past years, transcription factors binding to mitotic chromosomes have been suggested to play a role in the ability of stem cells to self-renew, but whether mitotic bookmarking plays a role in self-renewal of cancer stem cells is unknown. Here the candidate will explore the role of mitotic retention of oncogenic transcription factors in the ability of cancer stems cells to maintain their gene expression program over cell division. To tackle this question, the PhD candidate will learn and apply a broad set of approaches, such as live cell fluorescence microscopy, genomics approaches (ChIP-seq, CUT&RUN, ATAC-seq, RNA-seq), genome editing using CRISPR technology, optogenetics, and in vitro 3D culture and migration assays.

One PhD position (Marie Skłodowska-Curie fellowship) in a dynamic EPFL spin-off company developing innovative Organism-on-Chip technology and bioinformatics tools.

Background. Nagi Bioscience SA aims to revolutionize the way toxic and beneficial effects of molecules are tested today, by introducing the first “Worm-on-Chip” technology. It combines the use of microscopic worms (Caenorhabditis elegans), as validated in vivo model for drug and chemical screening, with the first laboratory device for their fully automated in vitro culture, treatment and analysis. Our technology allows “in vivo testing at the in vitro scale”, which is a sustainable alternative to traditional animal experimentation and key to boost innovation and efficiency within pharma, cosmetic, chemical industries and biomedical research.

The Team. Nagi Bioscience SA is located in the EPFL Innovation Park and works closely with laboratories of the SV and STI faculties of EPFL. Our team is highly interdisciplinary and comprises people with various backgrounds, including biologists, engineers and programmers. The future candidate will work at the interface of these different fields, with a specific focus on the biology and bioinformatics software development.

The Project. The candidate will (1) develop cutting-edge bioinformatics software for high-throughput analysis and interpretation of the data generated through our technology; (2) set up a comprehensive database (cleaning, transforming and classifying big data) for the computational prediction of the biological impact of drugs and chemicals on C. elegans and discoveries in the pharmaceutical research field; (3) contribute to the development of innovative “Organism-on-Chip” bio-assays. Candidates should have a solid background in bioinformatics, machine learning for biological data analysis and data mining, strong programming skills in C/C++, RStudio, python, and basic knowledge in biology and drug/chemical screening. Depending on the applicant interests, more workload could be allocated to benchwork or bioinformatics throughout the PhD project.

Keywords: start-up, alternative to animal experimentation, data science, machine learning, computational prediction, artificial intelligence, drug screening

Construction and analysis of a Lipid brain atlas

Neural cells produce thousands of different lipids, each endowed with peculiar structural features and contributing to specific biological functions. Lipid composition can affect neuron firing properties influencing vesicle fusion and fission processes, membrane conductivity, and ion fluxes. Nonetheless, a systematic and fine-grained characterization of lipid composition in the different brain regions is not available. The doctoral candidate will aim at filling this gap by building and analyzing a high-resolution atlas of lipid metabolism in the adult mouse brain. We anticipate that such an atlas will serve as a powerful tool for neurochemical research.

The project offered jointly by the La Manno and D’Angelo labs will allow the candidate to:

 – Use super-resolved Imaging Mass Spectrometry (IMS) to reconstruct the spatial lipidome in serial brain sections from adult mice.

 – Analyze the dataset generated to describe the vast heterogeneity and organize it into an atlas.

 – Develop ad-hoc algorithms (e.g., latent variable decomposition) for the analysis and interpretation of the data.  

 – Integrate the lipid atlas with gene expression brain atlases and build models that can predict one data type from the other.

 – Assess the lipid deregulation resulting from the progression Parkinson’s disease.

In the Neuroengineering Laboratory, we investigate transgenic flies (Drosophila melanogaster) to understand how behavior is controlled and to design more intelligent robots.

We have several PhD openings to study one of the following interdisciplinary areas:

(1) Data-driven neural and biomechanical modeling of limb control.

Key techniques: Computational modeling, Simulations, Confocal microscopy, Genetics

(2) Optical recordings of neuronal population dynamics for limb control.

Key techniques: 2-photon microscopy, Machine learning, Genetics

Join us! There is much to discover!”

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 and their application involving experimental studies. Projects involving internal collaborations between computational biologists, physicists and experimentalists in the lab are frequent. Specific research topics include the 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 modelling & 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.

  1. Investigation of centriole number control mechanisms in human cells
    Background
    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.
    Objective
    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.
    Approaches
    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.

  2. Exploring evolutionary diversity and origin of SAS-6 proteins
    Background
    Centrioles are small organelles that are critical for forming cilia, and which exhibit a striking 9-fold radial symmetric arrangement of microtubules. 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.
    Objective
    Develop machine learning strategies (e.g. using Tensorflow) for high throughput protein structure prediction. Apply machine learning to identify homologues of SAS-6 across all domains of life and thus help trace its origin. Proceed likewise for other proteins critical for centriole assembly and thus eventually trace organelle origin.
    Approaches
    Computational biology, machine learning, structural prediction; collaboration with the Dessimoz laboratory (UNIL, SIB).

starting at EPFL on February 1st, 2020 – Contact: [email protected] – Website: http://www.princeton.edu/~afbitbol/

(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.