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Paderborn University is a high-performance and internationally oriented university. Within interdisciplinary teams, we undertake forward-looking research, design innovative teaching concepts and
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the use of machine learning methods to process complex data sets. The focus is on techniques such as ultrasound, radar, computed tomography, acoustic emission analysis, and infrared thermography
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applied Machine Learning Hands-on experience with High Performance Computing Systems Basic knowledge of System Architecture of Supercomputers and NVidia-GPUs Practical experience with ML/DL workflows and
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consortia of cross-feeding bacteria ” • Generate and characterize bacterial mutants • Perform coculture experiments with different bacterial strains • Analyse the formation of clusters among bacteria using
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consortia of cross-feeding bacteria ” • Generate and characterize bacterial mutants • Perform coculture experiments with different bacterial strains • Analyse the formation of clusters among bacteria using
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and operation of the relevant equipment and systems regular participation in project meetings and active exchange with partner institutions documentation, publication, and presentation of the scientific
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | about 2 months ago
Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Area of research: Scientific / postdoctoral posts Starting date: 01.02.2026
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Germany Application Deadline 1 Oct 2025 - 11:56 (UTC) Type of Contract To be defined Job Status Other Offer Starting Date 4 Sep 2025 Is the job funded through the EU Research Framework Programme? Not funded
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the testing of newly devel-oped materials and the use of machine learning methods to process complex data sets. The focus is on techniques such as ultrasound, radar, computed tomography, acoustic emission
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Computer-adaptive methods and multi-stage testing Application of machine learning in psychometrics Predictive modeling of educational data Methodological challenges in cohort comparisons Advanced meta