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-wide traffic prediction Design physically consistent and interpretable machine-learning methods for dynamic traffic systems Test and validate prediction models using large-scale real-world traffic data
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community-led design. You will be part of the Making With cluster https://www.tue.nl/en/research/research-groups/making-with within the Department of Industrial Design. You will also work closely with
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project, an interdisciplinary and transdisciplinary research project funded by NWO, the Netherlands Research Council, under the call on KIC Consumer behaviour for a circular system transition. See: https
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PhD Scholarship Opportunity - Processing intelligence for green metals using in situ X-ray characterisation and machine learning Job No.: 693787 Location: Clayton campus Employment Type: Full-time
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University of Girona (UdG) - Institute of Computational Chemistry and Catalysis (IQCC) | Spain | 3 months ago
(CompBioCat) research group at the Institute of Computational Chemistry and Catalysis (IQCC, http://iqcc.udg.edu ), University of Girona (Spain), is seeking a highly motivated PhD candidate to join the ERC
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the Faculty of Engineering. Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/303995/ai-in-ship-design-constru… Requirements Research FieldEngineeringEducation LevelMaster Degree
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10 Jul 2026 Job Information Organisation/Company Delft University of Technology (TU Delft) Research Field Engineering » Design engineering Engineering » Materials engineering Researcher Profile
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https://www.uni.lu/snt-en/research-groups/trux/ . Your role Conduct cutting-edge research in Artificial Intelligence and Cybersecurity Design, implement, and run studies in human subjects research
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Infrastructure? No Offer Description This doctoral research project focuses on the design, synthesis and biological evaluation of novel nucleoside analogues with potential anticancer and antiviral activity. In our
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under global change requires accurate and consistent soil information at global scale. Current global soil maps are derived using empirical machine learning that often ignores known soil processes