About the position
The Schwarz Lab is actively seeking a motivated PhD student with a theoretical background and an interest in method development for cancer genomics.
About us
Our computational group is interested in understanding the role of chromosomal instability, somatic copy number alterations (SCNA) and chromatin structure in cancer evolution. We develop and apply algorithms for reconstructing and simulating cancer evolution in close collaboration with our clinical and experimental partners. We are part of several national and international consortia, such as the ICGC-ARGO, TRACERx, and SATURN3 consortia and hold close ties to BIFOLD, the Berlin Institute for the Foundations of Learning and Data.
About the project
As a successful candidate you will develop algorithms and models to investigate cancer evolution and chromosomal instability. A focus lies on the development of AI methods for improved phylogenetic reconstructions and feature space embeddings from complex genomics data. You will be part of CLONETRAC, a collaborative consortium with the aim of tracking the clonal dynamics of cancer through treatment at the single-cell level, funded by the Bruno and Helene Jöster Foundation. You will be embedded in a thriving interdisciplinary and collaborative team of computational biologists, computer scientists and clinicians at the University Hospital of Cologne.
About you
Ideal candidates will have a Masters degree in Physics, Mathematics, Computer Science, Bioinformatics or a related field. A strong background in theoretical and/or applied machine learning and AI, and a strong interest in medical research is expected. Prior experience in cancer genomics is a plus. Successful candidates will show a strong work ethic and should be enthusiastic about working in a highly interdisciplinary environment. Excellent communication skills and the ability to work in a team are required.
For further reading and our recent works see:
Kaufmann et al. 2022, Genome Biology
Watkins et al. 2023, PLOS Computational Biology
Kaufmann et al. 2026, bioRxiv
Tagged as: Computer Science, Data Science, Mathematics, Physics
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