Postdoc in Computational Genomics – Machine Learning for Multi-Omics Profiling of Cancer Evolution

Department: “Computational Genomics and Systems Genetics” and “Regulatory Genomics and Cancer Evolution”

Code number: 2024-0085

The German Cancer Research Center is the largest biomedical research institution in Germany. With more than 3,000 employees, we operate an extensive scientific program in the field of cancer research.

The research groups of Prof. Dr. Oliver Stegle (Division of Computational Genomics and Systems Genetics) and Prof. Dr. Duncan Odom (Division of Regulatory Genomics and Cancer Evolution) are looking for a postdoctoral researcher to join a collaborative project with the goal of deciphering clonal dynamics in cancer by integrating machine learning methods with large-scale spatial transcriptomics analyses.

Our research groups are internationally advancing single-cell and functional genomics technologies (Odom) and are pioneering computational methods for deciphering molecular variation across individuals, space, and time (Stegle). We have a track record in the development of computational methods for high-throughput omics data, machine learning for multi-omics integration and causal discovery. The postdoctoral researcher will work in a highly multidisciplinary setting between both laboratories at the German Cancer Research Center in Heidelberg, which is connected to the vibrant local ecosystem for data science, machine learning and computational biology in Heidelberg, including ELLIS Life Heidelberg and AI Health Innovation Cluster. 

Selected publications: 

• Velten, Britta, et al. Identifying temporal and spatial patterns of variation from multi-modal data using MEFISTO. Nature Methods (2022), 1-8. https://www.nature.com/articles/s41592-021-01343-9
• Kleshchevnikov, Vitalii, et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nature Biotechnology (2022): 1-11. https://www.nature.com/articles/s41587-021-01139-4
• Winkler, Tolkachov, et al. The cycling and aging mouse female reproductive tract at single-cell resolution. Cell (2024) 981-998 https://www.sciencedirect.com/science/article/pii/S0092867424000588
• Panten et al. The dynamic genetic determinants of increased transcriptional divergence in spermatids. Nature Communications (2024). https://www.nature.com/articles/s41467-024-45133-1

 

Job description:

You will be jointly affiliated with the laboratory of Prof. Dr. Stegle and Prof. Dr. Odom to generate novel insights into how clonal competition across space and time can shape tumor inception and cancer development in vivo. Your activities will comprise:

• Designing and executing experiments to study in vivo cancer clonal evolution using spatial transcriptomics and single-cell multi-omics analyses
• Developing tailored machine learning and causal inference methodologies to analyse these datasets 

The project setup allows for some flexibility according to qualifications and interests, and we encourage candidates with strong expertise, which may vary from those listed, to apply.

Requirements:

• A PhD or equivalent qualification in computer science, statistics, mathematics, physics, and/or engineering, or a degree in biological science with demonstrated experience in computational and statistical development
• Experience with implementing machine learning or statistical learning (e.g. Bayesian inference, deep-learning) 
• Programming skills in Python and experience with frameworks like PyTorch, Keras, Pyro or TensorFlow 
• Motivation and dedication to lead a scientific project
• Supportive, creative and responsible team member
• Expertise in the analysis of omics data, genetics, statistical interpretation and analysis of next-generation sequencing datasets is a plus; we encourage applications also from candidates with little or no background in biology or medicine, and a keen interest to learn

We invite applications in all fields, from any eligible researcher regardless of nationality or gender. Our divisions stand for equal opportunities and diversity. We believe that diversity enables us to collaborate more effectively and be innovative in our approaches. Qualified female candidates are especially invited to apply.

We offer:

• Interesting, versatile workplace
• Excellent framework conditions: state-of-the-art equipment and opportunity for international networking at the highest level
• Remuneration according to TV-L incl. occupational pension plan for the public service and capital-forming payments
• 30 days of vacation per year
• Flexible working hours
• Possibility of mobile work and part-time work
• Family-friendly working environment, including parent-child room, counseling services, e.g. on Elder Care
• Sustainable travel to work: subsidized Germany job ticket
• Develop your full potential: access to the DKFZ International Postdoc Program and DKFZ Career Service with targeted offers for your personal development to further develop your talents
• Our Corporate Health Management Program offers a holistic approach to your well-being

Important notice:

The DKFZ is subject to the regulations of the Infection Protection Act (IfSG). As a consequence, only persons who present proof of immunity against measles may work at the DKFZ.

Earliest possible start date: as soon as possible

Duration: The position is initially limited to 2 years. ​

Application deadline: 24.04.2024

Contact:

Prof. Dr. Oliver Stegle
Phone +49 (0)6221/42-3598

Please note that we do not accept applications submitted via email.