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07.08.2025, Wissenschaftliches Personal The Chair of Computational Mathematics at the Technical University of Munich (TUM) invites applications for one PhD position. The Chair of Computational
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to pursue a doctoral degree (Ph.D.). Remuneration is 100% TVL E13 according to the German public sector rates. CCBE's interdisciplinary team is performing research in the broad field of computational methods
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morphology, and mechanical performance. This Ph.D. project focuses on the data-driven modeling and optimization of structural foams used in high-voltage battery systems for electric vehicles. As part of BMW’s
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, the cluster will be funded by the DFG as part of the Excellence Strategy of the German federal and state governments, with TUM serving as the administering university. The aim is to learn how to design and
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to the definition, implementation, and coordination of strategic priorities across the EUROfusion program. You will lead high-level initiatives, prepare key management documents, and act as a strategic interface
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of extension, depending on performance and project needs. **Qualifications** • For Doctoral Candidates: Master’s degree in Computer Science, Information Systems, or Mathematics. **Applicants must demonstrate
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expertise in developing and training machine learning models (ideally with a focus on LLM), high-performance computing, data management, and software architecture Strong Python programming skills and
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biomolecular structure, dynamics, and interactions in complex environments and live mammalian cells. By combining ultra-high-field NMR with complementary biochemical, computational, and cellular approaches
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Documented expertise in developing and training machine learning models (ideally with a focus on LLM), high-performance computing, data management, and software architecture Strong Python programming skills
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of distributed computing, machine learning, image and text analysis, randomized data structures, high-performance computing, and quantum algorithms. Beyond this research, we aim to support computational thinking