Bioinformatician / Data Engineer for the Translational Drug Response and Genomics Profiling Project

Bioinformatician / Data Engineer for the Translational Drug Response and Genomics Profiling Project Reference number: 2025-0332 Heidelberg Full-time Clinical Cooperation Unit "Pediatric Oncology" 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 Hopp Children's Cancer Center Heidelberg (KiTZ) is a joint institution of the DKFZ, Heidelberg University Hospital (UKHD), and the University of Heidelberg (UniHD). As a treatment and research center for oncological and hematological diseases in children and adolescents, KiTZ is committed to exploring the biology of childhood cancer and closely linking promising research approaches with patient care – from diagnosis to treatment and aftercare. The Clinical Cooperation Unit "Pediatric Oncology" and the Division of Pediatric Neurooncology at the German Cancer Research Center and the Hopp Children’s Cancer Center Heidelberg are jointly seeking a About the Project:Drug Sensitivity Profiling & Multi-Omics Integration Clinical drug sensitivity profiling and identification of novel biomarkers in both patient-derived and cell line datasets. Responsibilities include large-scale integration of multi-omics and drug sensitivity data, development and maintenance of cohort-based drug response classification applications, and scripting for automated drug screening workflows. The ultimate goal is to identify novel therapeutic targets and improve diagnosis, prevention, and treatment outcomes in pediatric oncologyThe project also involves exploration of high-content imaging datasets; therefore, experience or interest in AI-based image analysis would be advantageous.The position is jointly hosted by the groups “Translational Genomics” led by Dr. Robert J. Autry and “Functional Pediatric Precision Oncology” led by Prof. Dr. Ina Oehme.What's special about this position:An interdisciplinary research environment within one of Europe’s leading pediatric cancer research centersAccess to large-scale genomics and pharmacological datasetsCollaborative supervision and mentorship by both the Autry and Oehme groupsOpportunity to contribute to clinically translatable discoveries in pediatric oncologyYour Tasks:Develop, optimize, and standardize computational pipelines for multi-omics data processing (e.g., mutation calling, RNA-seq, methylation, and epigenomic profiling)Implement, automate, and maintain workflows for drug screening data analysis, including automated data quality control, hit calling, and sensitivity classificationIntegrate genomic, transcriptomic, and pharmacologic data across multiple platforms to identify therapeutic vulnerabilities and predictive biomarkersGenerate user-friendly applications and dashboards for data visualization and explorationCollaborate closely with wet-lab scientists, bioinformaticians, and clinicians to design data-driven analyses for translational studiesContribute to AI/ML-driven high-content imaging data analysis pipelines for assessing drug effects on cancer cellsConstant support of students and postdocs in basic computational and bioinformatics workflowsGenomic and functional screening approaches include, but are not limited to:Whole-genome sequencing, RNA-seq, ChIP-seq, single-cell and spatial transcriptomics, and large-scale drug response assays; imaging approaches include multiplex confocal microscopy and live cell imagingYour profile:Education: M.Sc. or Ph.D. in bioinformatics, computational biology, computer science, statistics, molecular biotechnology, or a related disciplineTechnical Skills:Proven experience with Linux/Unix, Python, R, and Bash scriptingStrong background in bioinformatics workflows, multi-omics integration, and/or machine learningExperience with high-performance computing environments is beneficialFamiliarity with AI-based image analysis and data visualization frameworks (e.g., Streamlit, Shiny, or Dash) is an assetScientific Expertise:Background in genomics, transcriptomics, or pharmacogenomics data analysisUnderstanding of drug screening and functional genomics dataKnowledge of biological data standards and reproducible research principlesSoft Skills:Strong communication skills and ability to present and document findingsCollaborative, supportive, curiosity-driven, and organized, with attention to detailFluent in English (German not required)Ability to work both independently and as part of a multidisciplinary teamApplication Process:Interested candidates should submit the following documents via our online application tool:Curriculum vitaeMaster’s and/or doctoral degree certificatesLetter of motivation