36 machine-learning-phd positions at Delft University of Technology (TU Delft) in Netherlands
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/InstituteDelft University of TechnologyCountryNetherlandsCityDelftPostal Code2628 CDStreetMekelweg 2Geofield Where to apply Website https://www.academictransfer.com/en/338964/phd-position-machine-learning-on-gra
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available validated DEM simulation models and enriched by operational equipment performance data. To this end, physics informed machine learning techniques will be used to bring model data and real data
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nonlinear interactions at the origin of such extreme events. In this project, we will explore the use of cutting-edge scientific machine learning framework that blends deep learning with physics-based
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. On this PhD project you will develop novel systems biology methods employing control and analysis of dynamical models, and machine learning models, in particular neural networks. The developed methods will be
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experience in data science and machine learning. A good command of spoken and written English Doing a PhD at TU Delft requires English proficiency at a certain level to ensure that the candidate is able
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following aspects will help you stand out: Knowledge of data-driven control algorithms, biomechanical modelling, system identification, machine learning, control theory. Prior experimental experience on human
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or at least some interest in machine learning, preferably techniques for management/adaptation of systems. Programming experience in MATLAB/Python or C/C++, preferably in relation to radar signal processing. A
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interest in machine learning, preferably techniques for management/adaptation of systems. Programming experience in MATLAB/Python or C/C++, preferably in relation to radar signal processing. A curiosity
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interest in artificial intelligence/machine learning applications. • You want to bridge the gap between research and industrial applications. Doing a PhD at TU Delft requires English proficiency at a certain
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discovery processes, LLMs help maintain an organized and dynamic knowledge base, supporting continuous learning and decision-making within organizations and beyond. This PhD research will also investigate