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scalable training and inference on parallel, distributed and GPU-accelerated computing systems Benchmark the developed approaches against established methods, assessing predictive performance, generalisation
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, computer science, mathematics, statistics, economics, or related disciplines, an interest for the application of operations research in energy systems and strong programming skills. Familiarity with parallel
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23 Sep 2026 Job Information Organisation/Company Constructor Knowledge Labs gGmbH Research Field Computer science Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application
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to work on Higgs data analysis within our ATLAS Muon Group. Our long-standing analysis program focuses on exploring the properties of the Higgs boson and searching for physics phenomena beyond the Standard
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that protect researchers, their data and their compute against a fast-evolving threat landscape. Your Tasks Design and operate our HPC clusters across four data centers, including scheduler (SLURM), parallel
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The Faculty of Mathematics, Informatics and Statistics, Ludwig-Maximilians-Universität München (LMU) and the Federal Statistical Office (Statistisches Bundesamt, Destatis) invite applications for a
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governing bodies: program design, briefings for the Director and the presenting teams, protocol and logistics, and structured follow-up Communications, exhibitions and visibility: You coordinate
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Max-Planck-Institut für Struktur und Dynamik der Materie | Hamburg, Hamburg | Germany | 2 months ago
researchers and SSU Computational Science to deliver effective computing solutions. Essential Qualifications: Experience in Linux system administration and HPC system administration. Experience with parallel
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Max Planck Institute for the Structure and Dynamics of Matter, Hamburg | Hamburg, Hamburg | Germany | 2 months ago
Qualifications Experience in Linux system administration and HPC system administration. Experience with parallel computing technologies and accelerator (GPU) computing. Familiarity with scientific software
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exploration and optimization of the process parameter space, as well as for adaptive, data-driven machine learning approaches to map the electrolysis process to a digital twin. In parallel, data workflows and