{"id":208900,"title":"PhD Student (m/w/d)","company":"TUM Klinikum Rechts der Isar","city":"München","region":"Bayern","location":"81675 München","work_model":"unknown","employment_type":"full_time","employment":["fixed_term","thesis"],"roles":[],"visa":"not_mentioned","relocation":"not_mentioned","posted_at":"2026-11-05T00:00:00+00:00","tools":["Python","R"],"seniority":"unknown","years_experience_min":null,"german_requirement":"not_mentioned","german_level":"unspecified","english_requirement":"required","english_level":"unspecified","team_language":"unknown","confidence":0.4,"method":"rules","language_badge":"Not stated","language_evidence":null,"requirements_snippet":null,"url":"https://www.arbeitsagentur.de/jobsuche/jobdetail/14225-9e9da088056d03c1-S","source":"bundesagentur","agency":null,"description_text":"Join an internationally recognized research team investigating the genetic foundations of sleep disorders through large-scale genomic analyses and machine learning. As the Krombach Fellow, you will contribute to a global multi-ancestry research consortium and help advance precision medicine approaches for restless legs syndrome.\n\nPhD Position (m/f/d) - Krombach Fellow\n\nMünchen | Full-time | As soon as possible | Fixed-term (36 months)\n\nDesign and conduct genome-wide association studies (GWAS) and large-scale multi-ancestry meta-analyses using international biobank and research datasets.\nApply advanced statistical genetics methods to identify and prioritize disease-associated variants, genes, and biological pathways.\nDevelop and benchmark machine-learning models for genetic risk prediction across diverse populations.\nIntegrate genomic, clinical, and functional genomics data to improve the understanding of sleep disorders and their biological mechanisms.\nBuild reproducible, version-controlled analysis pipelines on high-performance computing and cloud platforms.\nPresent research findings within an international consortium and contribute to high-impact scientific publications.\n\nMaster’s degree (or equivalent) in bioinformatics, statistics, mathematics, computer science, machine learning, human genetics, epidemiology, or a closely related quantitative discipline.\nStrong programming skills in Python and/or R to analyze and interpret large-scale genomic and health-related datasets.\nSolid understanding of applied statistics, including regression models, multiple testing, and causal inference, enabling robust analysis of complex biological data.\nExperience working with large datasets and high-performance computing environments to manage computationally intensive genomic analyses.\nInterest in human disease biology and motivation to translate genetic discoveries into clinically relevant insights through collaboration with clinicians and experimental researchers.\nExcellent written and spoken English skills to communicate findings effectively within an international research consortium and at scientific conferences.\n\nA temporary position with the possibility of extension\nEGYM well pass, corporate benefits and discounts (e.g. Käfer), cafeteria, sports and cultural offers, company bike\nWorking in the heart of Munich at Max-Weber-Platz with very good accessibility by public transport such as the subway, S-Bahn or tram\nCompany pension plan through the Federal and State Pension Institute (VBL)\nSelf-realization through a varied and technically demanding job with interdisciplinary cooperation between various departments of the TUM Klinikum","responsibilities_text":null,"requirements_text":null,"status":"open","duplicate_of":null,"first_seen":"2026-11-05T16:21:07.178550+00:00","responsibilities_summary":null,"requirements_summary":null,"all_tools":["Python","R"],"posting_language":"en","evidence":[{"language":"english","requirement":"required","quote":"Excellent written and spoken English skills to communicate findings effectively within an international research consortium and at scientific conferences.","source":"rules"}],"years_experience_max":null,"mobility_evidence":[],"model_version":"rules-9","english_badge":"English required","company_id":null}