Scientist in Computational Biology

Reference number: 2025-0298

  • Heidelberg
  • Full-time
  • Precision Sarcoma Research

The German Cancer Research Center (DKFZ) is one of Europe’s largest cancer research centers. “Research for a life without cancer" is the mission of our world-class scientists and all our team members.

The DKFZ is a place where the brightest minds pursue bold ideas and seek answers to pioneering scientific questions through collaboration, innovation, and exploration across many disciplines. We provide a dynamic environment which empowers excellence with state-of-the-art technologies, cutting edge infrastructure, and a global scientific network. 

Contribute your knowledge, vision, and dedication to create a space where scientific discovery in cancer research is transformed into benefits for human health.


The Precision Sarcoma Research Group at the German Cancer Research Center and the National Center for Tumor Diseases Heidelberg (Head: Dr. Priya Chudasama) is seeking for the next possible date a

We are seeking a computational biologist to join the efforts of the consortium “DECIPHER-M: Deciphering Metastasis with Multimodal Artificial Intelligence Foundation Models”. This 5-year consortium is funded by the German Federal Ministry of Education and Research (BMBF) under “Major unanswered questions in cancer research” in the framework of the National Decade against Cancer.

DECIPHER-M consortium is a multi-institutional effort to transform the understanding and clinical management of metastasis by creating foundation models using routinely collected clinical data, including pathology images, radiology imaging, electronic health records, and multi-omics data. This position offers an exciting opportunity to work at the intersection of cancer biology, spatial multi-omics, and artificial intelligence.

More information:
https://digitalhealth.tu-dresden.de/projects/decipher-m/
https://www.dkfz.de/en/precision-sarcoma-research

 

Your Tasks

The successful candidate will join a highly interdisciplinary team of molecular biologists, pathologists, oncologists, radiologists, and data scientists to play a pivotal role in developing multi-modal data analysis and integration workflows, as well as downstream dissection of metastasis-associated multimodal signatures derived from the foundation models to gain new biological insights.

Key tasks include, but are not limited to

  • Develop and implement computational pipelines for the analysis and integration of spatial multi-omics and other multimodal datasets
  • Design and fine-tune machine learning and deep learning models to extract meaningful patterns and predict metastatic behavior
  • Collaborate closely with experimentalists for mechanistic dissection of multimodal signatures to contribute to the identification of diagnostic or prognostic biomarkers or therapeutic targets
  • Lead the collaborative initiative and communicate the findings in joint meetings, publications, and at international conferences
  • Assist in the mentoring of team members taking on computational tasks

Your Profile

  • Master's degree or equivalent in bioinformatics, computational biology, cancer biology, or similar
  • Strong background in analyzing and integrating large-scale bulk multi-omics datasets; experience in single-cell and spatial-omics approaches will be considered an advantage
  • Proficiency in programming languages such as R and Python, bash scripting, and deep learning frameworks
  • Ability to acquire and process data from sequencing facilities or public resources, work with HPCs, run and adapt existing pipelines (in-house or nf-core) and develop new ones
  • Proficiency in building and fine-tuning machine learning and deep learning models for biological data analysis
  • Strong publication record, including lead authorships, with experience in collaborative research and supervision of students or interns
  • Comprehensive knowledge of cancer biology and cancer genomics, maintained through rigorous literature review and applied to data interpretation and project advancement
  • Demonstrated ability to work both independently and collaboratively, with high self-initiative, strong work ethic, and a team-oriented mindset

Applications must include a CV, cover letter, complete list of publications, references or recommendation letters, and expected availability date. Incomplete applications will not be considered. Please apply exclusively via the application portal. For any questions, please send an email to priya.chudasama@nct-heidelberg.de. 

We Offer

  Excellent framework conditions: state-of-the-art equipment and opportunities for international networking at the highest level

  30 days of vacation per year

  Flexible working hours

  Remuneration according to TV-L incl. occupational pension plan and capital-forming payments

  Possibility of part-time work

  Family-friendly working environment

  Sustainable travel to work: subsidized Germany job ticket

  Unleash your full potential: targeted offers for your personal development to further develop your talents

  Our Corporate Health Management Program offers a holistic approach to your well-being

Are you interested?

Then become part of the DKFZ and join us in contributing to a life without cancer!

Contact:

Dr. Priya Chudasama
Phone: +49 6221 42 1600

Duration: The position is initially limited to 1 year with the possibility of prolongation.
Application Deadline: 20.12.2025

Applications by e-mail cannot be accepted.
Please also note that we cannot return applications submitted by post.
 

We are convinced that an innovative research and working environment thrives on the diversity of its employees. Therefore, we welcome applications from talented people, regardless of gender, cultural background, nationality, ethnicity, sexual identity, physical ability, religion and age. People with severe disabilities are given preference if they have the same aptitude.

Notice: We are subject to the regulations of the Infection Protection Act (IfSG). Therefore, all our employees must provide proof of immunity against measles.

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