31 electrical-engineering-"https:" "https:" "https:" "https:" "UCL" PhD positions at Utrecht University
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15 Aug 2026 Job Information Organisation/Company Utrecht University Research Field Engineering » Civil engineering Engineering » Water resources engineering Environmental science » Earth science
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PhD position on Modelling of turbidity current fluxes in submarine canyons Faculty: Faculty of Geosciences Department: Department of Earth Sciences Hours per week: 36 to 40 Application deadline
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How can generative AI (GenAI) support Health Technology Assessment (HTA) of medicines? HTA bodies and technology developers are already experimenting with GenAI, but systematic evidence on where it
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PhD Position in Generative AI in Health Technology Assessment (HTA) Faculty: Faculty of Science Department: Department of Pharmaceutical Sciences Hours per week: 36 to 40 Application deadline
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community at the Bijvoet Centre and the Faculty of Science. Visit the Kaiser Lab and the Bijvoet Centre websites for more information. Where to apply Website https://www.academictransfer.com/en/jobs/364327
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the bachelor’s and master’s degrees programmes of the department at Utrecht University. Where to apply Website https://www.academictransfer.com/en/jobs/363281/phd-mixing-in-turbidity-current… Requirements Specific
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glycan arrays, engineered glycans, influenza binding assays, and—depending on expertise—molecular biology, enzymology, biochemical assays. Collaborative environment: you will join a multidisciplinary team
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of Sustainable Development at Utrecht University. Where to apply Website https://www.academictransfer.com/en/jobs/363888/phd-spatial-optimization-of-lan… Requirements Specific Requirements We are looking
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of migration narratives; With our support, you will be in charge of all aspects of data collection (from data scraping from online platforms, and recruiting participants to the design); You will use advanced
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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