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- Delft University of Technology (TU Delft); 17 Oct ’25 published
- Delft University of Technology (TU Delft); Published yesterday
- Delft University of Technology (TU Delft); today published
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of Technology (TU/e) is a leading research group dedicated to pushing the boundaries of knowledge in the field of physic-based computing. We are currently seeking a highly motivated PhD student to join our team
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high-impact journals and present at international conferences. Where to apply Website https://www.academictransfer.com/en/jobs/357571/phd-on-mixed-signal-circuit-des… Requirements Specific Requirements A
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Description This PhD position explores how AI agents can play games to generate meaningful gameplay data. You will work on reinforcement learning, automated feature engineering, and the comparison of AI- and
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, electrical engineering or simila,r with an affection for machine learning; You are an independent and original thinker with a creative mindset; You are a fast thinker with excellent analytical and
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has direct societal impact for improving healthcare environments and outcomes. Job requirements MSc Degree in Human-Computer Interaction, Industrial Design Engineering or related fields. Strong
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artificial intelligence, computer science, engineering, mathematics, physics, or a related discipline Demonstratable background in machine learning, information retrieval or natural language processing
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require new mathematical machinery. LOGSMS will combine diverse tools from discrete mathematics, learning theory and machine learning, thus facilitating the design and analysis of such models. PhD position
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the PhD physically at Thales, working directly with planners, engineers, and data teams on real production challenges. The PhD will be hosted by the Industrial Engineering and Management Sciences (IEMS
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of the Quantum & Computer Engineering (QCE) department is looking for a highly motivated PhD candidate who is eager to work on AI based solutions for predictive inteligence for MRI scanning. The candidate will
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candidate in the exciting area of multiscale and multiphysics modelling of sustainable fibrous composites, with additional focus on uncertainty quantification and machine learning. Information The context