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this change and explore cutting-edge methods in chemical, mechanical, and plasma processing of metal ores for a truly circular economy. Correlating experimental, ab initio, and multi-scale simulations, as
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07.04.2026, Academic staff PhD position at the interface of computational physics, machine learning, and experimental reactor design. The project focuses on developing PINN-based simulations
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phenomena by analyzing data from aircraft missions, conducting laboratory experiments as well as by developing and exploiting advanced simulation codes. Additionally, technological objectives include
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phenomena by analyzing data from aircraft missions, conducting laboratory experiments as well as by developing and exploiting advanced simulation codes. Additionally, technological objectives include
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well as LiDAR measurements, into ensemble agroecosystem model simulations. The successful candidate will play a key role in developing robust landscape-scale digital twins and advancing data assimilation
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and simulation modeling. A quantitative understanding of ecosystem dynamics provides the foundation for the development of robust management concepts for the sustainable provisioning of diverse
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position in the area of machine learning and computer simulations. The focus of the PhD project will lie on developing machine learning models for clustering, classification, regression and reinforcement
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and describe their impacts on biodiversity and ecosystem services. To do this we use a combination of diverse methods, from empirical research to remote sensing and simulation modeling. A quantitative
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partners as well as our partners from ICRISAT, India. Your tasks in detail: Extend the existing process-based model to allow the simultaneous simulation of 3D root architecture development, release of
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: Engineering design & CAE (e.g., structural/thermal/fluids workflows) Chemical and process industries (e.g. optimization, control, surrogate models) Related computational engineering problems where simulation