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Field
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expert knowledge in a reusable format. Numerical Representation, Develop numerical representations of ship designs that are interpretable by machine learning algorithms and suitable for generative ai model
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analytics (statistical models, machine learning, uncertainty quantification) to monitor and predict cycling travel conditions from various perspectives (safety, crowding, travel time, comfort, etc
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Interventions Our goal: To make digital health interventions more effective by predicting and improving adherence through Artificial Intelligence (AI) and machine learning (ML). Your colleagues
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expert knowledge in a reusable format. Numerical Representation, Develop numerical representations of ship designs that are interpretable by machine learning algorithms and suitable for generative ai model
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predicting and improving adherence through Artificial Intelligence (AI) and machine learning (ML). Your colleagues: An interdisciplinary team of scientists working across Maastricht University and FH Joanneum
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based models, including the deployment of machine learning algorithms. The project aims to have a tangible impact on the way urban waters are monitored, and the findings of your project will be
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-on monitoring with cutting edge data-driven and physical based models, including the deployment of machine learning algorithms. The project aims to have a tangible impact on the way urban waters are monitored
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, materials science, electrical engineering or similar; You have a fascination for unconventional device concepts and a strong affinity for materials physics and machine/physical learning; You are
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8 Sep 2025 Job Information Organisation/Company Eindhoven University of Technology (TU/e) Research Field Computer science » Computer hardware Computer science » Digital systems Engineering
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, modelling, and AI to turn scientific research into real-world impact. You’ll join an open, supportive environment that fosters learning and professional growth. Job requirements Must haves: Master’s degree in