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on the recently published DeepRVAT framework, which leverages advances in machine learning to learn an optimal rare variant aggregation function in a data-driven manner. You will have the opportunity to spend
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using DC and AC-based techniques such as characteristic curves, cyclic voltammetry and electrochemical impedance spectroscopy Participation in the investigation of stacks and cells post-test using
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of fluid dynamic modeling and the analysis of thermomechanical stresses, you will accompany the entire development process – from the optimization of electrochemical performance to the elaboration
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exchange and interaction state of the art computing infrastructure (HPC) salary assigned according to the pay scale UKF standard social benefits, e.g. UKF job ticket UKF Your tasks: develop and optimize
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Broadband UV-generation position Push the frontiers of nonlinear optics in multi-pass cells Optimize third harmonic generation in nonlinear gases Develop scaling concepts to kJ pulse energy levels General
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analysis, simulation, chemometrics, control engineering, artificial intelligence, soft sensors, and process sensors. We are always looking for new technologies and new methods to monitor and optimize
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genomics, virtual cell models Graph-based neural networks, optimal transport Biomedical imaging, deep learning, virtual reality, AI-driven image analysis Agentic systems, large language models Generative AI
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04.04.2025, Wissenschaftliches Personal The Chair for Computer Architecture and Parallel Systems (CAPS) offers this position as part of the DARE-project funded by the EuroHPC JU bringing together
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motivated PhD students, interns, and PostDocs at the intersection of computer vision and machine learning. The positions are fully-funded with payments and benefits according to German public service
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on the design and evaluation of innovative data- and machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization