Postdoctoral Position in Deep Learning for Structural Biology and Protein Design

Published
WorkplaceLausanne, Lake Geneva region, Switzerland
Category
Position



EPFL, the Swiss Federal Institute of Technology in Lausanne, is one of the most dynamic university campuses in Europe and ranks among the top 20 universities worldwide. The EPFL employs more than 6,000 people supporting the three main missions of the institutions: education, research and innovation. The EPFL campus offers an exceptional working environment at the heart of a community of more than 16,000 people, including over 12,000 students and 4,000 researchers from more than 120 different countries.

Postdoctoral Position in Deep Learning for Structural Biology and Protein Design


Your mission :
Project

The successful candidate will collaborate with both laboratories to develop and train deep learning models for modeling protein allostery and designing protein switches. Proteins are not static molecules. They often behave as switches, alternating between states that carry out distinct functions. Hence, switching is a powerful mechanism of biological regulation and typically occurs upon structural changes triggered by a wide variety of external stimuli (e.g. from photon absorption to the binding of another protein) through a process referred as allostery. Understanding how this switching behavior occurs at the molecular level remains challenging. The advent of deep learning offers new opportunities to explore and predict protein motions but these approaches have mostly been applied to static representations of protein structures. Here, the candidate will tackle the modeling of switching dynamics using specially designed geometric deep learning techniques applied to molecular representations. We expect that this approach will lead to a new understanding of the mechanisms underpinning these switches and, eventually, to generative approaches allowing to engineer novel molecular switches.

Our laboratories

?url=http%3A%2F%2Fbarth-lab.epfl.ch&module=jobs&id=2802607" target="_blank" rel="nofollow">?url=http%3A%2F%2Fbarth-lab.epfl.ch&module=jobs&id=2802607" target="_blank" rel="nofollow">http://barth-lab.epfl.ch :
We belong to the Institute of Bioengineering at EPFL and are also part of the Ludwig Institute for Cancer Research (LICR) in Lausanne. While EPFL provides a world class environment for basic science and engineering, the LICR fosters translational applications of basic discoveries to cancer medicine at the highest level. Our laboratory benefits from this dual exciting environment and interdisciplinary approaches are essential to our research. We work at the interface of computational biology, biophysics, chemistry, and cell biology to uncover the molecular principles that regulate protein and cellular signaling. Using this understanding, we (1) model and design novel protein biosensors and protein signaling networks for synthetic biology and engineered cell therapeutic applications; (2) design enhanced and selective molecular therapeutics; (3) predict the effects of genetic variations on protein structure/function and protein signaling networks for personalized cancer medicine applications. We are part of the Rosetta Commons, a community of developers of the software Rosetta ( ?url=https%3A%2F%2Fwww.rosettacommons.org&module=jobs&id=2802607" target="_blank" rel="nofollow">?url=https%3A%2F%2Fwww.rosettacommons.org&module=jobs&id=2802607" target="_blank" rel="nofollow">https://www.rosettacommons.org) , the premier suite for macromolecular modeling, and are actively developing novel computational approaches.

?url=http%3A%2F%2Flts2.epfl.ch&module=jobs&id=2802607" target="_blank" rel="nofollow">?url=http%3A%2F%2Flts2.epfl.ch&module=jobs&id=2802607" target="_blank" rel="nofollow">http://lts2.epfl.ch :
We are with the School of Engineering and our research focuses on developing Machine Learning methodologies. The laboratory environment is extremely multi-disciplinary, hosting projects in AI for computational biology, computational chemistry or neuroscience as well as more theoretical work. Our main expertise is in geometric deep learning and in particular machine learning on graphs, but we recently investigated generalized Implicit neural representations and other representation learning architectures that can handle multi-view data or dynamics.

Your profile :
PhD in Physics, Math, Chemistry or Computer Science. Ideal candidates will have strong programming skills in python, C/C++ python and demonstrated expertise in deep learning and macromolecular modeling. In addition, candidates should have a record of relevant publications in peer-reviewed international journals, the ability to speak and write effectively, strong team skills, be self-motivated, and creative.

We offer :
EPFL provides state-of-the-art facilities and is one of the leading technical universities worldwide. A competitive salary is offered.

Interested applicants should upload the application online in one PDF file, which includes a curriculum vitae, a statement of research interests, and names of three references.

Start date :
This position will open starting July 1, 2023.

Term of employment :
Fixed-term (CDD)

Duration :
1 year CDD, renewable

Remark : Only candidates who applied through EPFL website or our partner Jobup-s website will be considered. Files sent by agencies without a mandate will not be taken into account.

Reference :
Job Nb 2966

In your application, please refer to myScience.org and reference JobID 2802607.