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Field
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) design, based on diffusion and/or flow matching, that can rapidly adapt pre-trained models to new tasks and select optimally informative simulations at each stage in a design process. Collaborate closely
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Help revolutionize one of the world’s most carbon-intensive industries by developing cleaner, hydrogen-based processes for ironmaking. As a PhD researcher at TU Delft, you’ll contribute
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will provide fundamental insights that help optimize chemical recycling processes, bringing Europe one step closer to a truly circular plastics economy. This PhD project is part of a national research
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research on topics including, but not limited to: Continual learning, with a focus on novel optimization techniques and memory architectures. Advancing the reasoning capabilities of large language models
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We are looking for a strong, motivated PhD-student with an analytical, algorithmic background who is interested in pursuing a PhD-project devoted to the allocation of donor organs to patients. The goal
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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
to anticipate and manage. This PhD will develop a machine learning module to detect early warning signals of positive tipping points from techno-economic data, helping policymakers design adaptive strategies
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volatile geopolitics. Shortages, trade frictions, and financial mismatches can stall otherwise viable tipping dynamics and establish carbon-intensive lock-ins. This PhD will develop an agent-based inspired
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system evolution? We are offering six PhDs positions across the department of Mechanical Engineering, Electrical Engineering and Mathematics and Computer Science that focus on solving cutting-edge design
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can improve the image quality of quantitative MRI, it can be used in clinical routine to select the optimal treatment for each patient, greatly improving treatment outcomes worldwide. Therefore, in
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We invite highly motivated students with a strong background in systems and control to apply for a PhD/Postdoc position within the Control Systems Technology section at the Department of Mechanical