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substrate–catalyst–conditions combinations). The resulting experimental dataset on catalyst activity (TON, TOF) and selectivity will be used to develop predictive machine-learning models that enable accurate
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Location: Central Cambridge PhD Studentship - Marie Curie network ON-Tract: Protein engineering of enzymes: in vitro directed evolution and machine learning-based elaboration of biocatalysis
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candidate in the exciting area of multiscale and multiphysics modelling of sustainable fibrous composites, with additional focus on uncertainty quantification and machine learning. Information The context
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, etc.), and data-driven methods (optimisation, generative AI, agent-based modelling, machine learning). Our work provides decision support for policy makers, industry stakeholders, and researchers by
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data-driven methods (optimisation, generative AI, agent-based modelling, machine learning). Our work provides decision support for policy makers, industry stakeholders, and researchers by delivering
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PhD Studentship in Aeronautics: How offshore wind farms and clouds interact: Maximising performance with scientific machine learning (AE0078) Start: Between 1 August 2026 and 1 July 2027
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Are you interested in challenging deep learning at its core? And specifically, do you want to perform cutting-edge research and develop novel advances in hyperbolic deep learning for computer vision
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of Artificial intelligence, Machine learning, Numerical simulation, Formal verification. Such methods include, among the others: AI-guided simulation of the mathematical models of the patho-physiology and PK/PD
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, or a related field. Have documented experience in some of the following: Computational materials modelling or quantum mechanical simulations (e.g. DFT, MD). Machine learning / deep learning (preferably
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; A Curriculum Vitae (CV), if applicable, a list of publications and references; A course list with grades (both BSc and MSc); An IELTS-test, Internet TOEFL test (TOEFL-iBT), or a Cambridge CAE-C (CPE