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machine learning and computer simulations. The focus of the PhD project will lie on developing machine learning models for clustering, classification, regression and reinforcement tasks to work with
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Job related to staff position within a Research Infrastructure? No Offer Description PhD position on physics-based machine learning modeling for materials and process design Reference code: 2026/WD 1
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the DFG Priority Programme “Molecular Machine Learning” and embedded in the research project “Multi-fidelity, active learning strategies for exciton transfer in cryptophyte antenna complexes”. The PhD
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: Investigate and design optimal computing and communication architectures for hardware acceleration of large-scale machine learning workloads Perform characterization and modeling of electronic and optical
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expertise in the RTG-addressed PhD subjects, high interdisciplinary desire to learn and willingness to cooperate, very good verbal and written English communication skills as well as the absolute
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the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data
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, and training methods - across multiple technological platforms - photonics, electronics, biological neurons. Responsibilities and tasks This PhD project aims to develop, verify, and benchmark learning
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of Photogrammetry and Remote Sensing and together with other chairs being part of the RTG. Requirements: good or very good university degree in electrical engineering, computer science, computer engineering or
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the reference number 27697, via our online portal: Apply now via https://jobs.uksh.de/job/Kiel-PhD-%28mfd%29-Statistical-Genetics-Machine-Learning-Schl-24105/1279933701/ For more information visit: www.uksh.de
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by colloids, as well as methods for immobilizing these ions. Modern methods of theoretical chemistry (first principles, kinetic Monte Carlo, machine learning) will be applied to investigate diffusion