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, preferably involving fluid mechanics and/or microstructure related simulations. Experience with numerical simulations based on the finite element method (FEM) or the finite volume method (FVM). Experience with
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research on architectures and methods for the real-time delivery of EO data from dense nanosatellite/CubeSat constellations and to develop innovative GNSS-based sensing methods and AI models to detect a
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: Atmospheric AI Modeling and Forecasting This PhD position focuses on developing AI models that integrate real-time satellite communication signals, GNSS -based atmospheric observations, and numerical weather
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component, particularly magnetic components, Optimization and surrogate-modeling in Python, Integration of machine learning and numerical methods. We encourage applications from candidates with a strong
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numerical methods. We encourage applications from candidates with a strong interest in the topic, even if their background does not match every qualification listed above. Your competencies: For both PhD
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laboratory-, core- and animal facilities. The Department of Biomedicine focuses on innovation, entrepreneurship and collaboration with business and industry, and numerous researchers from the department have
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-efficient, open-loop optimisation of fermentation control profiles, building on recent theoretical developments in optimal control theory, reinforcement learning and numerical methods as well as laboratory
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and validated numerical models, to enable lightweight, reliable, cost-efficient, and sustainable novel design concepts. The PhD will also explore additively manufactured (AM) concepts with strong
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design and integrity management of the support structure of the subsea HVDC electrode unit, supported by experimental tests and validated numerical models, to enable lightweight, reliable, cost-efficient
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Job Description Are you passionate about using experimental physics and mathematical methods to accelerate the green transition and address climate change? Do you want to transform the way we