45 parallel-and-distributed-computing-"Multiple" PhD positions at Technical University of Munich in Germany
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earliest starting date is February 1, 2026. The successful candidate will be enrolled in the doctoral program of the Technical University of Munich. You can expect a dynamic and flexible work
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team. You will be enrolled in the TUM Graduate School with its structured doctoral program and professional-skills courses. You will contribute to an French-German collaboration on next-generation
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PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
of Munich (TUM), Campus Heilbronn. We are looking for exceptional candidates who are interested in pursuing a PhD in either theoretical computer science or graph and network visualization. We seek PhD
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insights into the dynamic distribution patterns of human tissue resident T helper cells across space and time. Topic: Dissecting the body-wide spatio-temporal organisation of human resident T helper cells T
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the use of machine learning methods to process complex data sets. The focus is on techniques such as ultrasound, radar, computed tomography, acoustic emission analysis, and infrared thermography
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an interdisciplinary career skills programme across Europe. The Technical University of Munich (TUM) is one of the best universities in Europe. It is characterised by excellence in research and teaching
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academic assignments at the chair What we look for in you Completed master’s degree in computer science, transportation, or related engineering fields Solid background in generative AI, machine learning, and
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Profile The ideal applicant has a strong background in bioinformatics or computational chemistry, as well as data analysis and solid English-language skills. Experience with programming is highly
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privacy-preserved fashion. Research topics include, but not limited to, i) handling distributed DL models with data heterogeneity including non i.i.d, and domain shifts, ii) developing explainability and
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privacy-preserved fashion. Research topics include, but not limited to, i) handling distributed DL models with data heterogeneity including non i.i.d, and domain shifts, ii) developing explainability and