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.
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.
We are interested notably in understanding the mechanisms governing asymmetric cell division and centriole assembly. To decipher these fundamental biological processes, we use a combination of experimental approaches, including functional genomics and live imaging, as well as cell free assays and computational simulations. Up to two PhD projects are available for motivated and talented doctoral students to join our exciting multidisciplinary team of scientists.
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.