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on building “bicycles for the mind”, algorithms that enhance (rather than automate) human capabilities. The Machine Learning Researcher serves as a computational scientist and technical lead, supporting
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in Chemistry, Physics or related subject. The selected PhD candidate will work with Prof Elena Besley on computational modelling of next-generation semiconductors made from atomically thin materials in
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together expertise in physics, chemistry, nanoscience, and materials engineering. For more information about IS2M, please feel free to visit the website: https://www.is2m.uha.fr/ . The PhD candidate (M/F
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Materials, mastering scientific machine learning, uncertainty quantification, and high-performance computing. Your models will inform fusion design and advance AI-for-materials. Perfect for physics, maths
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Deadline 8 May 2026 - 21:59 (UTC) Country Netherlands Type of Contract Temporary Job Status Not Applicable Hours Per Week 40.0 Is the job funded through the EU Research Framework Programme? Not funded by a
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and fostering close collaborations with leaders in materials science, physics, and chemistry to drive transformative advances in systems biology, biotechnology, and synthetic biology. Situated in Basel
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7 Apr 2026 Job Information Organisation/Company Technical University Of Denmark Department DTU Civil and Mechanical Engineering (DTU Construct) Research Field Engineering Computer science Researcher
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Centre for Synthesis & Chemical Biology, University College Dublin | Dublin Bar, Leinster | Ireland | 29 days ago
Extra Information PhD Studentship Job Status Full-time Hours Per Week 40 Offer Starting Date 1 Sep 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the
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recovery rate in the LIB recycling process and reduce waste and carbon footprint in the battery value chain. By returning waste to battery manufacturing, the project will make the battery value chain more
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) invites applications for a fully funded 4-year PhD program in Process Systems Engineering. The project aims at integrating data-driven optimisation and design of experiments techniques with model-based