PhD Student - Computational Biology / Bioinformatics

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

The German Cancer Consortium (DKTK)

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.