256 model-driven-development "Integreat Norwegian Centre for Knowledge driven Machine Learning" Postdoctoral positions in Netherlands
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for support in technology development. You will work closely with a PhD researcher at the German partner who focuses on the underlying machine learning models, and you will help coordinate the joint work across
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the sensors measured? In the ASPIRE project, we develop knowledge for the new generation of sports tracking technology that integrates athletes’ subjective experiences (such as perceived exertion, motivation
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Vacancies PostDoc Research Position on Developing Technology for Subjective Sporting Experiences Key takeaways Can technology learn to listen to how athletes feel, and not just to what the sensors
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OpenML (tasks and automatically share detailed workflows and model evaluations. More than just a platform, OpenML fosters a collaborative ecosystem where scientists create new tools, launch
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), to develop batteries for long-duration energy storage. Specifically, this postdoc position focuses on acid-base flow batteries, and more specifically the stack engineering in this system. Acid-base flow
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(AQUABATTERY, Elestor, Exergy Storage, RWE, Nobian), to develop batteries for long-duration energy storage. Specifically, this postdoc position focuses on acid-base flow batteries, and more specifically the
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or neural networks) on CFD data to develop a fast, data-driven wind field predictor. You will combine this surrogate model with AeoLiS and evaluate the accuracy of the new model setup by applying it to
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-driven approaches; · contribute to the development of dynamic digital twins for subsurface monitoring, forecasting, and decision support; · collaborate with researchers and external stakeholders, publish
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: to develop next-generation autonomous crop monitoring and decision-support systems for Controlled Environment Agriculture. By integrating plant sensing, data and crop models, we aim to enable more precise and
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forming fundamental challenges. Physics-based models are too complex, while pure data driven models lack explainability, ultimately reducing the robustness. The proposed solution is the development of a