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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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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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bargaining agreement: §48 VwGr. B1 Grundstufe (praedoc) Limited until: 30.04.2029 Reference no.: 5311 Your responsibilities: As a University assistant, you will contribute to the work group Machine Learning
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from working closely with its team of post-docs, associated researchers and partners (that range from Microsoft Research to the NHS). For this project you should have a strong interest in AI/Machine
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project, please email Erika Mancini (e.mancini@sussex.ac.uk ) or John Spencer (j.spencer@sussex.ac.uk ) References: https://www.sussex.ac.uk/research/centres/sussex-drug-discovery-centre/ https
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nationally, statistics from 2019–2024 show that only around 26% progress beyond Entry level (CEFR A1; National Centre for Learning Welsh, 2025). Increasing these progression rates is a key aim of the Welsh
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electron laser facilities. Contact: For informal enquiries please contact Sam Hay (Sam.Hay@manchester.ac.uk ) or Mike Hough (michael.hough@diamond.ac.uk ). https://www.manchester.ac.uk/research/sam.hay https
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. Experience in coding (e.g., Python/R/Matlab) and experience in behavioural experimentation, statistics, or machine learning is desirable but full training will be provided. Interviews for this studentship
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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling
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Project description Electromagnetic (EM) sensing is emerging as a powerful enabling technology for modern high-value manufacturing. Advances in computing power and machine learning now allow us to