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through to large-scale individual-based simulation as well as statistics and Bayesian inference. This highly motivated, collaborative research group leads funded, international consortia in modelling, NTDs
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and most comprehensive dataset on multiple sclerosis (MS), encompassing longitudinal data from over 40,000 individuals, some tracked for more than a decade. You will be responsible for advancing and
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with the possibility of renewal. This project addresses the high computational and energy costs of Large Language Models (LLMs) by developing more efficient training and inference methods, particularly
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, including (but not limited to): advanced Bayesian techniques to calibrate and update models In an adaptive setup, where decisions ought to balance active learning with exploitative goals; data-driven model
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technologies, causal association and diagnostic inference, virology for clinical trials (e.g., anti-viral pharmacodynamics), and serology or immunodiagnostics. The role includes support for establishing and
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image data to infer brain aging and injury mechanisms; and iv) study the potential relationship between exposure to head impacts and the development of neurodegenerative diseases such as Alzheimer's
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to infer brain aging and injury mechanisms; and iv) study the potential relationship between exposure to head impacts and the development of neurodegenerative diseases such as Alzheimer's, Parkinson's and
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substantial expertise in applied infectious disease epidemiology, causal inference, public health, or community-based intervention studies. You will also demonstrate a strong record of leadership and mentorship
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rendering into medical imaging workflows. A major focus will be on accelerating inference and training using GPU-optimised components, including custom CUDA kernels. This role offers a unique opportunity to
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, aerosols, and their interactions, utilising satellite-based remote sensing. Potential areas of emphasis include cloud tracking and the analysis of data from the innovative EarthCARE satellite mission in