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become better versions of themselves. At BMS we do this through academic education, fundamental science and societal problem-solving. From Bachelor’s or Master’s degrees and Professional Learning
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develop models that can read, extract and acquire knowledge from legacy data, coming both in the form of text and in the form of structured data (e.g. physical measurements) to predict characteristics
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willingness to learn Dutch. We prefer that you are proficient in scientific computing (Python, Matlab, R, and UNIX), have strong data processing and collection, and are familiar with the basics of machine
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physics and permeability evolution models from µCT data using machine learning and computational tools (PuMA/CHFEM/MOOSE) validated against experimental observations Bridging scales from pore-level
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Applications. The candidate will be embedded in the Massivizing Computer Systems (MCS) group, which focuses on research in distributed computing systems and ecosystems, and currently spans over 40 diverse people
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shifts in cell state and cell fate. Integrate spatial transcriptomics data to anchor these predictions in tissue context. Develop machine learning methods (e.g. graph neural networks, variational
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boundaries of system-level modelling, analysis, design, exploration and synthesis beyond the current state-of-the-art? Or are you curious to learn more about the application of AI for system diagnostics and
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mathematical control theory, and a keen interest in machine learning to apply for the PhD position within the Dynamics and Control section at the Department of Mechanical Engineering, Eindhoven University
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the SCM section will fill a maximum of one PhD positions this year that can be on any of these topics. Machine learning for stochastic last-mile deliveries In recent years, stochasticity has received
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shifts in cell state and cell fate. Integrate spatial transcriptomics data to anchor these predictions in tissue context. Develop machine learning methods (e.g. graph neural networks, variational