82 professor-computer-"https:"-"https:"-"https:"-"https:"-"https:" positions at Technical University of Munich
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12.01.2026, Academic staff The Professorship of Machine Learning at the Department of Computer Engineering at TUM has an open position for a doctoral researcher (TV-L E13 100%; initial contract 1.5
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. The project is jointly supervised by Prof. Dr. Ralf Jänicke (Institute of Applied Mechanics, Technische Universität Braunschweig) and PD Dr. Stefan Kollmannsberger (Chair of Computational Modeling and
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present them at international conferences Collaborate closely with researchers at TUM and partner institutions in Brazil Requirements A Master’s degree in physics, computer science, Earth system sciences
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Scientific and operational leadership of the Protein Design Accelerator in the Technology Hub Development and maintenance of computational pipelines and scripts for protein design workflows, including design
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qualified women. About the position The position contains both teaching duties and participation in research projects. The research project topics focus on improving object recognition through computer vision
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. Experience in programming (in particular, Python) and an interest in machine learning, data analysis, or scientific computing are expected. Prior experience with machine learning or optimization methods is
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something new — complemented by teamwork and fostering personal relationships. From assistants, students, researchers, postdocs to professors; we are all working hand in hand, are highly committed, and
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learning, medical image computing, biomedical engineering, medical physics, or related field Strong Python and PyTorch experience Solid publication record and ability to communicate research results
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the development of novel optical technologies addressing significant biological and medical challenges. The Mission: The successful candidate will play a key role in an ambitious research program focused
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research program focused on developing next‑generation multimodal imaging systems spanning the mesoscopic to microscopic scale. As part of a major research project and supported by extensive national and