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gene gain/loss events, horizontal gene transfer, and functional diversification within gene families. You will apply statistical models and machine learning algorithms to identify associations between
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for manufacturing operations. Process control: process modelling, control, and optimization, with applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in
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Command line proficiency and BASH scripting capabilities o Demonstrated experience with machine learning and/or statistical modeling o Expertise in handling large-scale, complex datasets with strong
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skills Desirable criteria Experience of atomistic modelling of ferroelectric materials Experience in development and application of machine learned potentials * Please note that this is a PhD level role
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of atomistic modelling of ferroelectric materials 2. Experience in development and application of machine learned potentials * Please note that this is a PhD level role but candidates who have submitted
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navigation algorithms and machine learning models on physical robot platforms. We are particularly interested in candidates with expertise in generative AI and curriculum learning applied to robotics, as
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develop statistical and machine learning models to identify and validate predictive biomarkers of resistance evolution in Pseudomonas aeruginosa lung infection. As part of this work, the postholder will
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Postdoctoral Research Assistant in Design for Behaviour Change: Circular Economy for Medical Devices
contribute to the strategic vision and long-term planning of Oxford’s research in technical AI governance. You should possess a PhD/DPhil in machine learning, computer science, engineering, or a closely
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well as good understanding of work in LLM evaluation and fine tuning. Experience in working on reasoning and explainability of NLP and machine learning models is desirable. About the School of School
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to reconstruct subsurface defects; Implement image/signal‑processing or machine‑learning pipelines for automated flaw characterisation; Collaborate with the Federal University of Rio de Janeiro, including short