Postdoctoral Researcher in Computational Genomics - Heidelberg, Deutschland - Deutsches Krebsforschungszentrum

Deutsches Krebsforschungszentrum

Geprüftes Unternehmen

Heidelberg, Deutschland

vor 3 Tagen

Geschrieben von:

Lena Wagner

beBee Recruiter

Beschreibung

Position:
Postdoctoral Researcher 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:

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:

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 singlecell multiomics analyses
Developing tailored machine learning and causal inference methodologies to analyse these datasets

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, deeplearning)
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

We offer:

Interesting, versatile workplace
Excellent framework conditions: stateoftheart equipment and opportunity for international networking at the highest level
Remuneration according to TV-L incl. occupational pension plan for the public service and capitalforming payments
30 days of vacation per year
Flexible working hours
Possibility of mobile work and parttime work
Familyfriendly working environment, including parentchild 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 wellbeing

Important notice:

Earliest possible start date:
as soon as possible

Duration:
The position is initially limited to 2 years.

Application deadline:

Contact:

Prof. Dr. Oliver Stegle
Phone /42-3598

The DKFZ is committed to increase the proportion of women in all areas and positions in which women are underrepresented.

Qualified female applicants are therefore particularly encouraged to apply.