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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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of Digital Agriculture is engaged in simulation models of plant stands to improve plant system understanding and its control and optimization. Our research links Artificial Intelligence methods
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) Good knowledge of AI frameworks like TensorFlow, PyTorch, Keras Good to have: Scientific publications Experience with ML models and methods (CNNs, LLMs, Transformers, GNNs) We offer: An optimal research
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develops and applies methods for uncertainty quantification, engineering reliability, and risk & decision analysis to support optimal and sustainable decision-making in engineering and environmental systems
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main focus on the development of control software. ▪ You will design and implement advanced control and readout protocols and optimize experimental characterization workflow,s leveraging machine learning