EDCB Open positions

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 hiring days: June 2022 – Deadline for applications: April 15, 2022

In the past few years, studies have begun to demonstrate the potential of personalized nutrition in controlled settings, but their generalizability to the broader population remains an open question. The Digital Epidemiology Lab is harnessing the power of the digital health approach to understand the link between dietary patterns and blood glucose response, and its mediation through the gut microbiota and physical activity. Based on a novel, very large data set from the Swiss “Food & You” digital cohort, we are training algorithms to create personalized diets with the goal to lower postprandial glucose response (PPGR). In the future, we are planing intervention studies to test the validity and scalability of this approach.

The source of the data, the Food & You cohort study, is a globally unique “digital cohort” on personalized nutrition in Switzerland, developed and run by the lab. As of March 2022, the cohort has collected detailed behavioral and biological data from over 800 people within Switzerland (~1000 participants expected by the end of 2022). The data comprises detailed, high temporal resolution nutritional data (at least 14 consecutive days of detailed food tracking data with images), physical activity data, gut microbiota data, and data from continuous blood glucose sensors, all collected simultaneously, in addition to demographic data. The study has created an unparalleled dietary dataset of close to 400,000 food item records with a total of over 33 million kcal.

We are now interested to use this data to develop recommender systems, to address the role of geographic location on diet, and to answer the question of the optimal number of data collection days needed to comprehensively capture an individual’s diet. Because the data was collected before and during the COVID-19 pandemic, we are also able to test the hypothesis that the COVID-19 pandemic had an impact on food consumption patterns in Switzerland.

We are looking for a talented and motivated PhD student to participate in this project, and, based on interest, also work on the continued development of machine learning models for nutrition tracking.

Your mission
Checkpoints arrest the cell cycle when cells are damaged. However, checkpoints also ‘fail’ or ‘give up’ after long arrests. This phenomenon is thought to be critical for biology and medicine but is poorly understood. By combining novel engineered protein-based optogenetic tools, molecular biology, genetics, and microscopy, we want to understand how checkpoints tell time at the molecular level. In a collaboration between the Rahi lab, where you would be performing molecular biology centered on yeast and recording timelapse microscopy movies, and the Barth lab, where you would be designing new optogenetic allosteric switches for checkpoint proteins using computational protein design, we will explore how checkpoints read out DNA damage and decide to arrest for specific amounts of time before ‘letting go’.

Your profile
Candidates should have a Msc in biophysics, bioengineering, or related disciplines and be experienced in molecular and/or cell biology, and computer programming.

Contact
For further information and application, please contact directly [email protected] or [email protected] .

Life on earth at all scales (societies, behavior, physiology, molecular functions) is temporally organized along the 24h daily cycle. This project builds on our longstanding interest to combine computational and experimental approaches to understand gene regulatory mechanisms underlying circadian rhythms, and notably their impacts on temporal physiology. 

In particular, we aim at integrating time-resolved functional genomics datasets in organs to model how the circadian clock and/or environmental cycles impinge on gene regulation across multiple levels, from transcription to translation to protein accumulation.

We will focus on statistical and machine learning models combining multiple types of RNA-seq and other omics measurement to dissect the dynamics of gene expression, including the mechanisms governing RNA transcription, accumulation and its translation.

The work is computational (although, depending on the interest of the student, performing validation experiments would be a plus) and highly interdisciplinary, combining concepts/tools from gene regulation, bioinformatics, statistics and machine learning.

For more information please contact [email protected]  

Links to representative recent publications form our lab, illustrating the questions and methodologies that will be further developed: 

Mermet, Genes Development 2018, http://genesdev.cshlp.org/content/32/5-6/347

Yeung, Genome Research 2018, https://genome.cshlp.org/content/28/2/182 

Wang, PNAS 2018, https://www.pnas.org/content/115/8/E1916 

Gobet, PNAS 2020, https://www.pnas.org/doi/10.1073/pnas.1918145117

Phillips, MSB 2021, https://www.embopress.org/doi/full/10.15252/msb.202010135

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.

The Vandergheynst-lab at the Ecole Polytechnique Fédérale de Lausanne (EPFL) has an opening for PhD student to join the lab’s emerging activities in data science and machine learning for applications in computational biology. This position is funded in the context of a large collaborative grant “Integrated multi-scale analysis of translation: single-molecules, omics and computation” with Jeff Chao (FMI Basel) and Felix Naef (EPFL). 

We are looking for a highly motivated new team member to further develop data processing and modeling tools for bulk and single-molecule translation experiments, in particular self-supervised deep learning methods for single molecule imaging.

We are looking for interdisciplinary profiles holding a MSc in molecular or computational biology or Computer Science (or a closely- related field) with a strong a strong interest in applications to biology. The successful candidate will work closely with our partners in the grant and will have to chance and freedom to collaborate on other exciting projects in a truly multidisciplinary environment leveraging machine learning and data science to accelerate scientific discovery.

 

The Oates lab is exploring how spatio-temporal patterns emerge at the tissue level from noisy cellular and molecular interactions using a population of genetic oscillators in the zebrafish embryo termed the segmentation clock. This multi-cellular clock governs the rhythmic, sequential, and precise formation of embryonic body segments, termed somites, and exhibits a rich set of spatial and temporal phenomena spanning from molecular to tissue scales. Defects in this clock underlie human congenital mal-segmentation disorders (hereditary scoliosis).

Although the segmentation clock has been the dominant paradigm for 20 years, this model does not account for a fascinating classical result: the heat-shock echo, in which periodic segment defects recur, like an echo, along the axis. The interval separating the defects is 5 segments, but – critically – there are no known multiple-segment periodicities in the segmentation clock. This suggests that something fundamental is still missing from our overall picture of segmentation.

Using innovative microscopy techniques, transgenic zebrafish, biochemistry, mechanical manipulation, deep sequencing, physical modeling and good old-fashioned heat-shocks, we aim to discover the mechanism underlying the repeated defects. We will characterize and investigate phenomena during the defects recently observed in our lab at multiple scales: single cell, synchronization between neighbor cells, large-scale wave patterns and mechanics of the tissue. We will also search in an unbiased way for genes that predict the echoes. If these sound to you like interesting questions and approaches to be explored in a challenging and interdisciplinary PhD, please apply to the program and contact the Oates lab via [email protected].

https://www.epfl.ch/labs/leb/

The physical properties of the mitochondrial matrix. ERC-funded project. The current dogma is that the mitochondrial interior, or matrix, behaves as a viscous fluid, albeit one with a complex shape. Interestingly, it has been reported that in vitro, different respiratory states of mitochondria correlate with differences in mitochondrial matrix viscosity, ultrastructure, and density. Fluorescence-based ratiometric, anisotropy, and recovery methods have been applied to measure its viscosity, but with results varying over two orders of magnitude. Intriguingly, motility and internal structure have been linked to metabolic states. More recently, it was reported that the internal ‘temperature’ of mitochondria is adaptive, and reaches nearly 50 °C when they are metabolically active. The field missing a comprehensive study that considers the mitochondrial matrix as a responsive complex fluid with potential for complex or non-equilibrium state behavior, the goal of this project.

https://www.epfl.ch/labs/lbm/

At least 2 openings are available for the April 15, 2021 deadline. PhD projects will be available in the following two areas of research:

  1. 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.

  1. 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.

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.