202 cloud-computing-"https:" "https:" "https:" "https:" "https:" "St" positions at Technical University of Munich in Germany
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. In the ELUD research project, we address the question of if and when learning agents converge to an efficient equilibrium and when this is not the case. ELUD will design new algorithms for computing
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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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. 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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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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-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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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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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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for an interdisciplinary “bridge-builder” who strives for Scientific Excellence and Real-World Purpose. ● Background: HCI (Human-Computer Interaction), Computer Science, Ethnography, Sociology, or a related
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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
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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