76 computational-physics "https:" "https:" "https:" "https:" "UCL" uni jobs at Technical University of Munich
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application, you confirm that you have acknowledged the above data protection information of TUM. Kontakt: contact.btd@ls.tum.de More Information https://www.lse.ls.tum.de/btd/home/
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06.02.2026, Academic staff The Laboratory of Chemical Process Engineering at TUM's Campus in Straubing is looking as soon as possible for a Ph.D. student (f/m/d) for Oxygen Management in
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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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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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, prototyping, facilitation, and knowledge visualization, with a strong interest in transformative technologies and societal change. Requirements: Necessary: Design process & methodological expertise — ability
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copies of official documents, as we cannot return your materials after the application process is complete. For more detailed information, please visit our Homepage: https://www.epc.ed.tum.de/td
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Regulation (GDPR)). By submitting your application, you confirm that you have acknowledged the above data protection information of TUM. Kontakt: office.cm@mgt.tum.de More Information https://www.fa.mgt.tum.de
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submitting your application, you confirm that you have acknowledged the above data protection information of TUM. Kontakt: office.lea@sot.tum.de More Information https://tumapply.aet.cit.tum.de/job/detail
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targeted methods for optimizing biomaterial properties. Tasks • Develop and produce fungal composites using wood-based substrates and characterize material properties (mechanical, physical, thermal) in
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information of TUM. Kontakt: contmech@mw.tum.de More Information https://www.epc.ed.tum.de/ddmm/aktuelles/article/phd-position-in-collaboration-with-bmw-on-data-driven-modeling-of-structural-foams-for-high