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First Stage Researcher (R1) Country Netherlands Application Deadline 11 Jan 2026 - 22:59 (UTC) Type of Contract Temporary Job Status Not Applicable Hours Per Week 38.0 Is the job funded through the EU
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), biostatistics, machine learning, data science and research data management, and causal inference methods (Iris Pigeot, Marvin Wright, Vanessa Didelez), and etiologic and molecular epidemiology (Konrad Stopsack
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specifically, do you want to perform cutting-edge research and develop novel advances in hyperbolic deep learning for computer vision? Then check out the vacancy below and apply for a PhD position in this
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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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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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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
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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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24 Nov 2025 Job Information Organisation/Company Delft University of Technology (TU Delft) Research Field Engineering » Aerospace engineering Engineering » Computer engineering Researcher Profile
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creation that controls clogging patterns Developing predictive digital rock physics and permeability evolution models from µCT data using machine learning and computational tools (PuMA/CHFEM/MOOSE) validated