204 cloud-computing-"https:" "https:" "https:" "https:" "https:" "St" "St" positions at Technical University of Munich in Germany
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part of an international team Desired Qualifications Familiarity with cloud computing platforms and large-scale data processing German language skills Publications in computer vision or machine learning
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. Your qualifications: Master’s degree in Aerospace Engineering, Mechanical Engineering, Computer Science, Electrical Engineering, or a related field. Strong interest and commitment to pursuing a Ph.D
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they interact and connect with each other. The doctoral researcher will develop computational indicators that capture these patterns from digital communication data, model how learning relationships form and
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Informatics Initiative (MII)/FHIR standards Design and implement methodological concepts and software for benchmarking frameworks for AI evaluation Independently develop and implement research ideas within
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-cell communication, and cellular plasticity—all without destroying the sample. (https://www.cell.com/cell/fulltext/S0092-8674(25)00288-0 , https://www.biorxiv.org/content/10.1101/2024.11.11.622832v1
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, certificates, references, and publications) by 13 April 2026 to the following form: https://wkf.ms/4beNUNa Equal opportunity: The Technical University of Munich seeks to increase the proportion of women in
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lively intellectual community of 80+ researchers from numerous disciplines and fields of specialization. As a department, we deliver 2 Master’s programs and design STS content for the School’s PhD program
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04.02.2026, Academic staff The successful candidates will be part of the Munich Climate Center and the Earth System Modelling group at TUM (https://www.asg.ed.tum.de/esm/home/) and will be closely
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tailored computational methods are needed. This project aims at combining probabilistic machine learning methods with prior knowledge in the form of graphs to analyze and predict food-effector systems. Key
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molecular level. To yield new insights into food-effector systems, sophisticated and tailored computational methods are needed. This project aims at leveraging graph-theoretic approaches to analyze and