91 computer-"https:"-"APOS-UFFICIO-CONCORSI-DOCENTI" "https:" "https:" "https:" "https:" "https:" positions at Technical University of Munich in Germany
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in cancers of unknown primary (CUP). Your Role You will join Subproject 3 (Model Alignment and Optimization), led by PD Dr. Keno Bressem (https://scholar.google.com/citations?user=wIEgwbkAAAAJ&hl=en
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9 Feb 2026 Job Information Organisation/Company Technical University of Munich Department Computer Engineering Research Field Technology » Communication technology Researcher Profile First Stage
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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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office.ethics@mh.tum.de https://get.med.tum.de/ www.tum.de If you apply in writing, we request that you submit only copies of official documents, as we cannot return your materials after completion
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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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knowledge of the German language besides English. If interested, please send your full application to the email adress provided below. At the Mechanics & High Performance Computing Group, there is an open
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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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-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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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