180 parallel-and-distributed-computing-phd positions at Technical University of Munich
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on the cutting-edge field of Human-Centered AI Technologies using advanced Generative AI and novel interaction technologies such as VR/AR and Eye Tracking. PostDoc and PhD Positions in Human-Centered AI
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of Computation, Information and Technology at the Technical University of Munich (TUM) welcomes applications for a PhD or Postdoc Position (m/f/d, 100%, 2 years+) in Numerical Mathematics. Field of Research
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15.04.2025, Wissenschaftliches Personal The Lab for Artificial Intelligence in Medical Imaging (www.ai-med.de) is inviting applications for a fully funded PhD position in interpretable machine
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04.04.2025, Wissenschaftliches Personal The Chair for Computer Architecture and Parallel Systems (CAPS) offers this position as part of the DARE-project funded by the EuroHPC JU bringing together
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at the workplace, from compute and GPU servers to supercomputers Opportunity for a PhD (Dr. rer. nat.) in one of the group’s diverse research areas Salary according to the public service pay scale (TV-L E13
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Enrollment as a doctoral candidate in TUM IGGSE, with extensive training program Interested? Then we look forward to receiving your application; please send them by e-mail to jobs.ens@ed.tum.de with “PhD
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://mediatum.ub.tum.de/doc/1696192/aab7jokzk7x4paq7m2y9pa2p6.Wetzlinger-2022-NAHS.pdf Job Specifications For PhD applicants: Excellent Master’s degree (or equivalent) in computer science, engineering, or related
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, NeRFs, Diffusion Models, LLMs, etc. PhD and PostDoc Positions in Visual Computing & AI The Visual Computing & Artificial Intelligence Group at the Technical University of Munich is looking for highly
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(E13 TV-L, 100%, 3 years, starting July 2025) 07.04.2025, Wissenschaftliches Personal The Professorship of Political Economy (Prof. Dr. Timo Seidl) invites applications for a PhD position involving
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systems on different temporal and spatial scales. For our Research Group Applied Optimization we are looking for a Research Associate / PhD: Physics-informed deep learning for PDE-constrained optimization