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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 that 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.
Together with selected university partner sites, the DKFZ has established the German Cancer Consortium (DKTK) . The alliance between the DKFZ as the core center and the university partner sites in Berlin, Dresden, Essen/Düsseldorf, Frankfurt/Mainz, Freiburg, Heidelberg, Munich, and Tübingen operates joint translation centers at the University Medical Centers of each of the selected partner sites. The DKTK contributes significantly to filling the translational gap between basic and clinical cancer research, thereby developing new strategies for personalized oncology in prevention, diagnosis, and treatment of cancer. DKTK conducts transformative translational cancer research, educates the next generation of translation-oriented medical and clinical scientists and establishes strong pipelines towards clinical cancer research and the economy.
For the Institute of Molecular Oncology and Functional Genomics (https://www.translatum.tum.de/translatum/forschungsgruppen/rad-roland-experimentelle-krebsgenetik/) at the DKTK partner site Munich, starting at the earliest date possible, we are seeking aPhD Student in Computational Biology / Bioinformatics
Reference number: 2026-0194
We study the molecular basis of cancer through an integrated approach spanning genomics, molecular biology and computational biology, thereby aiming to better understand how cancers arise, spread or resist to therapy. See examples in: Nature 2018 Feb 1;554(7690):62, Cancer Cell. 2023 Jul 10;41(7):1327; Nature Methods. 2024 Apr;21(4):584. Nature. 2026 Feb 25. doi: 10.1038/s41586-026-10187-2.
By developing and applying genetic technologies for high-throughput applications in cells and mice, including CRISPR or transposon tools for massively parallel screening, we create and integrate multi-dimensional data to interrogate biological systems in their full complexity. Our team brings together cancer biologists, molecular geneticists, bioinformaticians, and clinicians who collaborate closely in a creative, multidisciplinary environment.
Your Tasks
Our open research areas include:
- Genomic and screening technology development
- Computational large-scale data integration
- Cell fate transitions in development and cancer
- Molecular mechanisms of cancer evolution
- Non-coding RNA biology
We are seeking a highly motivated PhD candidate in computational biology/bioinformatics to investigate fundamental questions in cancer biology through the analysis and integration of large-scale transcriptomic, genomic, and functional genomics datasets. The successful candidate will pursue an independent research project while collaborating closely with experimental and computational researchers. The position is 100% on-site.
Your Profile
- MSc degree in bioinformatics, computational biology, or a related field, or a life science degree with substantial computational biology training and programming experience
- Demonstrated hands-on experience in computational biology through a master's thesis, internship, or research project
- Experience working in a Linux/Unix environment and familiarity with command-line tools and Bash scripting
- Experience with R and/or Python for biological data analysis
- Solid understanding of applied statistics and statistical methods commonly used in transcriptomics and genomics research
- Knowledge of bioinformatics methods for processing and analyzing transcriptomic and/or genomic data, including bulk RNA-seq, single-cell RNA-seq, and DNA sequencing; practical experience with these methods is considered an advantage
- Knowledge of multi-omics data integration and machine learning/deep learning approaches for the analysis of complex biological systems
- Strong interest in molecular, cellular, and cancer biology, with enthusiasm for working at the interface of computational and experimental research
- Fluency in English is required
We Offer
- Excellent framework conditions: state-of-the-art equipment and opportunities for international networking at the highest level
- Access to international research networks
- Doctoral salary with the usual social benefits
- 30 days of vacation per year
- Possibility of part-time work
- Flexible working hours
- Family-friendly working environment
- Sustainable travel to work: subsidized Germany job ticket
- Unleash your full potential: targeted training and mentoring through the DKFZ International PhD Program and DKFZ Career Service
- Our Corporate Health Management Program offers a holistic approach to your well-being
Contact
Prof. Dr. Roland Rad
Phone: +49 89 4140-4374
The position is initially limited to 3 years.
Application Deadline: October 8, 2026
Applications by e-mail cannot be accepted.
Please also note that we cannot return applications submitted by post.
Are you interested?
Then become part of the DKFZ and join us in contributing to a life without cancer!
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, personal 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.
Deutsches Krebsforschungszentrum | Im Neuenheimer Feld 280 | 69120 Heidelberg | www.dkfz.de
Bitte beziehe dich bei deiner Bewerbung auf jobvector
und verwende die folgende Referenznummer:
2026-0194
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