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across the University of Oxford and the Oxford Liver Cancer Centre of Excellence to improve outcomes in HCC. You will oversee the preclinical models of liver cancer and administrative activities, in line
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, generating process, material, and structural data to support model development, validation, and process consolidation. Develop and apply physics-based and data-driven modeling frameworks to describe, predict
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systems that integrate explainable on-device AI and small language models (SLMs) to support human operators in making faster, safer, and more confident decisions. You will collaborate with a
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learning, large-scale model optimization, and generalization. To explore scalable optimization methods for large-scale, distributed, and multi-node collaborative training. To conduct theoretical analysis
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generative modeling, with a focus on flow matching and related techniques. To produce publications in top conferences and journals. To offer guidance and assistance to any students involved in the project
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these processes influence susceptibility to infectious and inflammatory diseases. We integrate state-of-the-art mouse models, high-dimensional immune profiling, single-cell genomics, and functional immunology
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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computational modelling, system development, and experimental studies. Publish and disseminate research findings through high-quality journal and conference publications, presentations, and other scholarly
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/nanofabrication and materials characterization tools; computational multi-physics/electromagnetics modelling and eigenmode analysis for photonic crystals; terahertz/microwave far-field spectra measurements or near
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Full-time, 3-year fixed term opportunity, located on the Camperdown Campus at the School of Geosciences Work on one of the world's most ambitious Earth system modelling initiatives Base Salary