Postdoc (m/f/d)

• Independently design, implement, and execute computational workflows for cancer genomics, single-cell RNA sequencing, and spatial transcriptomics.
• Analyze somatic mutations, copy-number alterations, structural variants, and mutational signatures to investigate tumor evolution and therapeutic resistance.
• Characterize cellular heterogeneity, cell states, cell-cell interactions, and spatial organization within the tumor microenvironment, including the integration of single-cell and spatial datasets.
• Integrate genomic, transcriptomic, spatial, and proteomic datasets to identify molecular mechanisms, candidate drivers of disease progression, and potential therapeutic targets.
• Develop, adapt, and optimize analytical approaches, applying appropriate statistical and machine learning methods.
• Work closely with wet-lab scientists to interpret computational findings, formulate mechanistic hypotheses, and prioritize candidates for functional validation.
• Take ownership of computational projects, critically evaluate results, communicate findings to interdisciplinary collaborators, and contribute to manuscript preparation and publication.

• We are looking for a candidate with strong expertise in cancer genomics, in single-cell and spatial transcriptomics, or in both. Candidates with a strong background in one of these areas are encouraged to apply, provided they are motivated to collaborate across disciplines and expand their expertise into the other.
• A PhD in computational biology, bioinformatics, cancer genomics, or a related discipline.
• Substantial hands-on experience in cancer genomics (e.g., somatic variant calling, copy-number analysis, mutational signatures, and tumour evolution) and/or single-cell and spatial transcriptomics (e.g., analysis of cellular heterogeneity and integration of single-cell and spatial data), preferably in cancer research.
• Strong programming skills in Python and/or R, and experience running large-scale analyses on high-performance computing (HPC) systems.
• Demonstrated experience in statistical analysis, quality control, data visualization, and interpretation of complex biological datasets.
• The ability to plan and execute computational analyses independently, critically assess results, and draw biologically meaningful conclusions, with a strong interest in understanding cancer development, tumor evolution, and therapeutic resistance.
• Excellent communication and collaboration skills, including the ability to discuss complex computational findings with experimental biologists
• A strong track record of scientific publications in relevant peer-reviewed journals

Desirable Qualifications:

• Expertise in both cancer genomics and single-cell/spatial transcriptomics, or experience with multi-omics integration.
• Familiarity with relevant tools and frameworks, including Scanpy/Scverse/Seurat, SpatialData, Squidpy, NCEM, Mutect2, ASCAT, Battenberg, GRIDSS2, and DPclust.
• Experience with mutational and copy-number signature analysis, tumor phylogenetics, RNA editing, or machine learning and deep learning approaches for genomic and transcriptomic data.
• Experience with workflow management tools such as Snakemake or Nextflow, version control, and reproducible computational environments (e.g., Conda, Docker/Singularity).

• EGYM well pass, corporate benefits and discounts (e.g. Käfer), cafeteria, sports and cultural offers,
• Working in the heart of Munich at Max-Weber-Platz with very good accessibility by public transport such as the subway, S-Bahn or tram
• Company pension plan through the Federal and State Pension Institute (VBL)
• Self-realization through a varied and technically demanding job with interdisciplinary cooperation between various departments of the TUM Klinikum