873 computational-physics "https:" "https:" "https:" "https:" "UCL" uni jobs in Germany
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the same qualifications and skills.Please indicate any disability in your application. Selection process Please find further informations here: https://www.fh-potsdam.de/en/university-career/career
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the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description This interdisciplinary professorship within
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plasma physics (XGC, IPPL). Expected qualifications: A Master's degree in Computer Science or Applied Mathematics. Necessary knowledge: Modern C++, GPU computing with CUDA/SYCL, MPI, Krylov solvers
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will primarily work with cryostats, cryo-magnets, and dilution inserts to achieve temperatures down to the mK range. Your Profile: Master`s degree in physics or in a similar field, preferably with a PhD
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Profile: You are enrolled in a master`s programme in chemistry, physics, materials science or a comparable field of study and are currently planning to write your master`s thesis. Good prior knowledge
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W3 Endowed Professorship for “Hemodynamic Modeling in Atherosclerosis- (f/m/d) KSB Foundation W3 end
clinical application. The focus is particularly on photon-counting computed tomography (PCCT), 4D MRI flow imaging, and AI-supported analysis and modeling methods (e.g., CT-FFR, predictive software models
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information on professorships, hiring processes, and the German academic system can be found here: https://uni-tuebingen.de/en/213700. Questions regarding the Cluster of Excellence can be directed
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to Germany, from which countries, and at what cost. Within this Master’s thesis, you will analyze potential import options for various hydrogen-based energy carriers. To this end, you will model process chains
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electronically at https://www2.mathnat.hu-berlin.de/NumAnaParDE in one single pdf-file. Since we will not return your documents, please submit copies in the application only. Your application must include a
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, graph neural networks, physics-informed ML) to approximate PF results Train models using simulation results generated from conventional power flow solvers Evaluate AI-based approximators in terms