EDBB Open positions

Barth lab

Expanding the universe of protein functions for synthetic biology and biomedicine

Our lab is developing and applying hybrid AI-based computational/experimental approaches for engineering classes of proteins with novel 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 and AI-based approaches for macromolecular modeling and design. Ultimately, we aim to develop a versatile tool for designing novel potent, selective therapeutic molecules, synthetic proteins, receptor biosensors, networks and pathways for reprogramming cellular functions. We are also affiliated to the Ludwig Institute for Cancer Research in Lausanne.

Projects in the lab are often multidisciplinary and involve the development of novel methods (e.g. Feng, Nat Chem Biol 2016; Nat Chem Biol 2017; Paradis, Nat Comm 2022; Dumas, biorxiv 2023) and their application involving experimental studies (e.g. Young, PNAS 2018; Chen, Nat Chem Biol 2020; Yin, Nature 2020; Keri et al., biorxiv 2023; Jefferson, Nat Comm 2023). Projects involving external collaborations with other research groups around the world or internal collaborations between computational biologists, physicists and experimentalists in the lab are frequent. We also actively translate our findings to the clinic in collaboration with physicians (e.g. Dr. Arber, Coukos from the Ludwig Institute for Cancer Research). Specific research topics include: 1. The design of protein biosensors, mechanosensors and signaling receptors for reprogramming cell (e.g. CAR T cell) functions and enhance cell-based therapies; 2. The design of highly selective and potent protein and peptide-based therapeutics towards challenging targets such as GPCRs or ion channels; 3. The study, prediction and design of protein dynamics and allostery using AI and classic computational approaches; 4. The development of novel AI-based algorithms for modeling & design of protein structures, interactions and motions.

Dry lab candidates should have strong programming skills in python/C/C++ and expertise in the development of deep learning methods. Knowledge in structural biology, bioinformatics, computational biomolecular modeling including molecular dynamics simulations is a plus. Candidates more oriented towards the wet lab should have strong skills in molecular and cell biology including experience in protein biochemistry, mammalian cell culture, microscopy, and structural biology. Hybrid computational / experimental projects are also possible.

LBNC

We are looking to hire a graduate student in cell-free synthetic biology. The prospective graduate student will work on building the foundations for the development of a synthetic cell. This project will involve developing state-of-art techniques and approaches in cell-free synthetic biology combined with microfluidic technologies to push the current boundaries of in vitro synthetic biology and cell-free transcription – translation systems. The graduate student will be embedded in a highly international and dynamic research environment.

The Laboratory for Biomedical Microfluidics (LBMM, www.epfl.ch/labs/lbmm) develops new approaches in antibody discovery and personalized cancer therapy.

The group is very interdisciplinary, including people with a primary training in biology, bioinformatics and engineering. Powerful technology platforms in the field of biomedicine and genomics have been developed over the past years, also leading to the establishment of two startup companies (www.veraxa.de and www.besttherapyforme.com).

Having a comprehensive microfluidic toolbox at hand (and expanding it continuously), we are now inviting applications for two projects:

1. Highly multiplexed RNA-seq biomarker discovery for drug sensitivity in leukemia. Here we plan to combine our Combi-seq approach (Mathur et al., Nature Communications 2022) with single-cell phenotypic cell sorting for drug sensitivity. 
2. Library vs library screening of T-cells and antigen-presenting cells for novel immune oncology approaches”. Here we aim to set up a platform for screening cell pairs for affinity. The project is carried out in the context of a large Sinergia grant consortium, including several bioinformatics groups.

In Tang lab, we leverage the power of metabolic and cellular bioengineering, synthetic chemistry and material engineering, and mechanical engineering to achieve controllable modulation of immune responses against diseases. The incoming student will work on projects in which we reprogram immune cell metabolism with engineered proteins, cells, and molecules for enhanced proliferation, function, and longevity (Metabolic immunoengineering), or investigate and manipulate the mechanical properties and interactions at molecular, cellular and tissue levels for enhanced immunotherapy (Mechanical immunoengineering).