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methods such as high-throughput experimentation to expediate the syntheses (and bioanalysis) of life-saving pharmaceuticals. The subsequent data will then be used to populate machine learning models
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filled The overarching aim of this project is to find synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application
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from oil spills and extraction remain a major concern, requiring effective strategies to reduce both immediate and long-term impact. Conventional remediation methods, such as skimming, in-situ burning
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to reduce both immediate and long-term impact. Conventional remediation methods, such as skimming, in-situ burning and dispersants, are well proven over the decades and provide scenario-specific mitigation
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Department: Faculty of Science AI Doctoral Training Centre Programme length: 42 months Closing date: 19 April 2026 The Faculty of Science AI Doctoral Training Centre (DTC) invites applications from
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spray drying are typically reliant on trial-and-error workflows, a narrow selection of polymers, and analytical methods that are unsuitable for early-stage screening due to high material demands. Moreover
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reliant on trial-and-error workflows, a narrow selection of polymers, and analytical methods that are unsuitable for early-stage screening due to high material demands. Moreover, no reliable predictive
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to cover living costs; Join a multidisciplinary cohort to benefit from peer-to-peer learning and transferable skills development. Learn more about the programme, available projects, and the application
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network performance. Resilience evaluation will consist of proposing a method for the best use of resource allocation, which will make water networks robust and resilient to these threats. Summary: Open to
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(School of Medicine), Teaching Associate – thomas.bestwick-stevenson@nottingham.ac.uk This project is not funded, and we are seeking a student who can self-fund the PhD. Programme description: The overall