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that integrate multi-omics data to uncover mechanisms of disease, cellular resilience, and therapeutic response. The post holder will lead research applying large-scale machine learning and foundation models
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Internal candidates will be given priority for this recruitment and must apply via the Employee Dashboard. About the Role The Oxford Applied and Theoretical Machine Learning group at the Department
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Digitalising populations of structural systems using machine learning (S3.5-MAC-Dardeno)
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and research grants (appropriate to career stage) Expertise in advanced quantitative and/or qualitative research methods, including AI or machine-learning based approaches. Ability to design, deliver
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, Engineering, or a closely related discipline. You will be a materials or physical scientist with a strong track record in applying deep learning to computer vision problems, ideally within battery
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. You will then develop a predictive Machine Learning tool to support engineers in incorporating vegetated systems into design stage decision making. Finally, you will apply Life Cycle Analysis
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. The project integrates synthetic organic chemistry, kinetic analysis, automation, and machine learning to establish next-generation mechanistic workflows for asymmetric organocatalysis. The project advances
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low-progression backgrounds, refugees, care-experienced learners, and those with military connections Use data and insight to evaluate impact, inform strategy, and continuously improve outreach activity
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: machine/deep learning, numerical modelling, statistics, optimisation, scientific computing • Ability to work across disciplines and collaborate with academic and industrial teams Desirable: • Experience in
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. The research will integrate advanced full-field imaging techniques, including X-ray computed tomography, neutron tomography, and related methods, with modern machine-learning approaches such as sparse regression