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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description In this project, we develop a new mathematical framework to efficiently model
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AI PhD Scholarship - Opportunity: Foundation Models for Brain Data Job No.: 696618 Location: Clayton campus Employment Type: Full-time Duration: The scholarship may be held for up to 3.5 years
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A PhD studentship is available for commencement October 2026 – January 2027 as part of the first Cohort of a new EPSRC Centre for Doctoral Training in UK Semiconductor Industry Future Skills* led by Swansea University in partnership with the University of Leeds. The PhD studentship will be: 4...
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Fully Funded PhD Position: Decoding Chromosome 1 Risk in AMD Using Stem Cell–Derived Retinal Models Host: Newcastle University (UNEW), UK Consortium: MSCA Innovative Training Network Pandora Start date
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Primary Supervisor: Dr Ke Li Foundation models trained on DNA and RNA sequences are beginning to transform genomics, but their predictions often remain difficult to interpret. A model may identify a
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-level power, thermal and energy-efficiency models for future heterogeneous computing systems built on advanced CMOS technologies What you will do Continued technology scaling and the integration
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11 Jul 2026 Job Information Organisation/Company University of Amsterdam (UvA) Research Field Biological sciences » Biological engineering Biological sciences » Biology Computer science » Modelling
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2 PhD Positions in Computational Modelling of Biological Membranes and Adaptive Functional Materials
at the University of Münster invites applications for two fully funded PhD positions in computational modelling of biological and functional material. The two projects address complementary questions in biological
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robust management of large traffic networks under all conditions. You have the most important role in this ambitious project as one of the young talents in our team. We have 2 PhD and one postdoc positions
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a mixture of computational, analytical and machine learning approaches to model the heat transfer to fuels and their physical and chemical behaviour, including changes in chemistry and physical