Saturday, 23rd March 2024, Job by TU Munich, GreenTodays :
Job Summary
- Published on: Saturday, 23rd March 2024
- Designation: Postdoc AI in regulatory genomics
- industry: Miscellaneous
- Vacancy: 1
- Employment Status: Full-time
- Job Location: Munich
- Salary: 0
- Gender:
- Application Deadline: Saturday, 23rd March 2024
Job Description
The Chair of Computational Molecular Medicine, led by Prof Julien Gagneur, develops computational approaches to study the genetic basis of gene regulation and its implication in diseases. Applications of our work range from understanding basic mechanisms governing gene expression to unraveling genetic aberrations triggering rare diseases and cancer. More at https://www.cs.cit.tum.de/cmm/home/.The projectEPIC is a 10-million Euro project funded for 6 years by the European Research Council under the ERC Synergy grant scheme. EPIC brings together 3 groups at the edge of complementary technologies – Vicente Pelechano (Karolinska Institute) for the development of high-throughput omics assays, Kevin Verstrepen (VIB-KU Leuven) for synthetic biology, and Julien Gagneur for the AI and bioinformatics. EPIC aims at deciphering the complete regulatory code, from chromatin to protein abundance, of the eukaryote S. cerevisiae. To this end, EPIC takes a unique attack angle leveraging evolution. We will assay 100 fungal species spanning over hundreds of millions of years across all layers of gene regulation and under different growth conditions. Moreover, EPIC will design nearly a million sequences to systematically test and refine our model of the regulatory code and eventually design genes and cells for biotechnological applications. Our research group will develop new AI modeling paradigms to learn from such multi-modal and multi-species data effectively. This will allow us to unravel complex regulatory instructions and their evolution, build predictive models, and design genes with intended regulation
Responsibilities + Skills
Education
large language modeling of DNA and RNA sequencesself-supervised and semi-supervised modeling strategiesMulti-modal modeling, spanning all levels of gene regulation from chromatin accessibility to protein abundance via transcript boundary choice, RNA stability, and localization.
Experience