Senior scientific software developer for AI in regulatory genomics

Senior scientific software developer for AI in regulatory genomics

18.03., Wissenschaftliches Personal

About us

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

The project

EPIC 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 strategy to study gene regulation by leveraging evolution. We will assay fungal species spanning 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.

Your role

As the senior computational biologist of the team, your role is to ensure a robust and scalable ecosystem for the entire project, covering, the design and implementation of :

• the data backend
• automated processing workflows including QC and reproducibility metrics
• APIs and packages supporting the development of new algorithms spanning large
• language modeling of DNA and RNA sequences, and algorithms for MPRA and gene design
• the project data portal to browse data and model outputs
• You will interact with other postdocs and be supported by more junior colleagues (PhD candidates, Bachelor’s and Master’s students).

Requirements

• PhD degree in Computational biology or related (Computer Science, Physics) or at least 3 years of experience in scientific software development
• Experience with NGS data processing and modeling
• Experience with deep learning frameworks
• Scientific software development
• Excellent organizational skills and good mentoring capabilities
• Fluent in English. Can communicate clearly in a multidisciplinary collaborative setting

We offer

The position can start on July,1st . It is funded until June, 31st

Salary according to the TV-L (German academic salary scale) - E14, the highest salary scale before group leader. It includes social benefits (healthcare, pension, and unemployment insurance)

Possibility to develop research management skills and move to an open-end position

International, attractive, and interdisciplinary working environment. The TUM CS department, among the top-ranked in Europe, gives access to excellent colleagues and students

Flexible working hours and home-officing policy

Disabled applicants with equal suitability and qualification will be given particular consideration

The TUM is striving to increase the proportion of women and hence applications from women are therefore expressly welcomed

Application

Applications should include a cover letter, CV, transcripts, and references and must be sent to by Sunday, April 14th referring to EPIC - Postdoc 1 in the email title.

We process and store your application files in accordance to the Art. 13 General Data Protection Regulation (GDPR) for the collection and processing of personal data (scroll down for the English version).

By submitting your application, you confirm that you have read TUM's data protection information.

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