7 machine-learning-"https:" "https:" "https:" "https:" "https:" "University of Cambridge" scholarships
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collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in biologically-inspired deep learning and AI
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numerical models and machine learning tools to predict loads, assess structural responses, and identify damage under extreme conditions. By combining computational simulations with data-driven approaches
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elaboration of biocatalysis for synthesis. A doctoral position is available for 3-years to work with Professor Florian Hollfelder at the Biochemistry Department of Cambridge University (https
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, release kinetics under biologically relevant triggers. The successful candidate will work at the interface of organic synthesis, chemical biology, and machine learning to guide linker design and optimise
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economic assessments machine learning or proxy-model based methods field scale simulation geological features geomechanics reactive flow The PhD fellow are not expected to master all these topics. Project
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. In addition, you must have: a solid foundation in energy technology and a strong understanding of artificial intelligence (AI), machine learning (ML), and data-driven modeling documented experience
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: 90 IELTS – International English Language Testing Service. Minimum result: 6.5 Certificate in Advanced English (CAE) or Certificate of Proficiency in English (CPE) from the University of Cambridge PTE