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Field
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FPGAs, CGRAs, and many Machine Learning accelerators, offer significant opportunities for improving performance and energy efficiency compared to traditional CPUs/GPUs. Yet, porting and optimizing code
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DIFFER: Science for future energy At the Dutch Institute for Fundamental Energy Research (DIFFER) we work on a future in which clean energy will be available to everybody, anywhere in the world. DIFFER’s mission is to perform leading fundamental research on materials, processes, and systems for...
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Science and Engineering at the Faculty of Mechanical Engineering, in close collaboration with the Netherlands Defence Academy. The CTE group is a dynamic team of researchers, including PhD candidates
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PhD Studentship in Aeronautics: How offshore wind farms and clouds interact: Maximising performance with scientific machine learning (AE0078) Start: Between 1 August 2026 and 1 July 2027
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/356373/phd-in-machine-learning-for-mat… Requirements Specific Requirements To get started in this role at DIFFER it is important: You have Master’s degree in machine learning, computer science, chemistry
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: vibration measurement and analysis, AI/machine learning or signal processing Programming experience (e.g. Python, MATLAB, or similar) A creative and analytical mindset with an eagerness to pioneer new methods
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engineering Engineering » Systems engineering Researcher Profile First Stage Researcher (R1) Country Netherlands Application Deadline 4 Jan 2026 - 22:59 (UTC) Type of Contract Temporary Job Status Not
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PhD Candidate in Exposomics, Machine Learning and Artificial Intelligence Faculty: Faculty of Veterinary Medicine Department: Department Population Health Sciences Hours per week: 36 to 40
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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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make extensive use of low-fidelity simulations which can provide fast but inaccurate solutions depending on the flow complexity. To close this gap, this PhD will explore machine-learning (ML) methods