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13.03.2026, Academic staff A fully funded PhD position (initially 75% TV-L E13, increasing to 100% TV-L E13 after 8 months) is available at the Professorship of Optimization and Sustainable Decision
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therefore a key challenge for the green and digital transition. In this project, you will apply state-of-the-art AI methods to optimize functionalized membranes for selective rare-earth recovery, helping
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and optimizing the transport of magnetic particles in fluid flows, with applications in advanced resource extraction and separation technologies. Your Research Project The project combines data-driven
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in cancers of unknown primary (CUP). Your Role You will join Subproject 3 (Model Alignment and Optimization), led by PD Dr. Keno Bressem (https://scholar.google.com/citations?user=wIEgwbkAAAAJ&hl=en
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, 100% funded PhD student position to fill starting around June 2026. Research is to be in the field of computational methods in nonlinear and large scale optimization / inverse problems or in novel
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–2 years, total 3–4 years) on data-centric research for foundation models. This project investigates how to optimize training data shaping foundation model capabilities - including data curation
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Development: Develop and optimize biopolymer-based fiber spinning processes (e.g., dry-jet wet spinning, core shell fiber spinning) using materials such as cellulose and proteins. Establish and refine
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biological models. This position involves the optimization, and operation of an innovative multimodal microscope, as well as close collaboration with experts in the biological sciences for its application
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of optimization, inverse problems, or sensitivity analysis. Familiarity with surrogate models (ROMs, ML-based surrogates). Motivation for renewable energy and wind turbine technology. What We Offer Fully funded
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the corresponding production processes and environments to be automatically initialized, processing areas to be generated and the optimal processing tools and their alignment to be assigned to them. For this purpose