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Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg | Magdeburg, Sachsen Anhalt | Germany | about 1 month ago
applications for a PhD Student (f/m/d) position. The Max Planck Institute in Magdeburg is a high-profile research institution. Research topic: "Enhance Activities in Computational Homogeneous Catalysis”. This is
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Research Framework Programme? Other EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Reference code: 50156134_2 – 2025/MO 4 Commencement date: March
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Your Job: Quantum computers will play a crucial role in the development and optimization of battery materials in the future. In this project, you will develop innovative quantum algorithms
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computational optimization of additively manufactured Mg alloys for biodegradable implant applications. Host: Helmholtz-Zentrum Hereon, Geesthacht, Germany, with PhD degree awarded by Kiel
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performance. They apply and develop a broad range of interdisciplinary technologies ranging from genetics and genomics to structural biochemistry, advanced imaging and computational and mathematical modelling
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position (contract-based) and one PhD fellowship in Computational Biophysics/Chemistry (see also https://constructor.university/comp_phys). The PhD position is focused on efficient algorithms
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-edge Machine Learning applications on the Exascale computer JUPITER. Your work will include: Developing, implementing, and refining ML techniques suited for the largest scale Parallelizing model training
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Description Where: At the Chair for Computational and Theoretical Biology ( CCTB ) and the Center for Artificial Intelligence and Data Science ( CAIDAS ) of the University of Würzburg. Project
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09.12.2025 The Hector Research Institute of Education Sciences and Psychology at the University of Tübingen invites applications for the following position: PhD Position on Computational Modelling
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high-resolution, quantitative time-lapse soil property measurements using high-performance, parallel computing. Together with our existing rich dataset, we will inform a soil-plant digital twin, enabling