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digital twins using prediction-powered inference to enhance reliability assessment; The theoretical analysis and algorithmic development of methods rooted in statistical learning theory, multiple hypothesis
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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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Ellison Institute of Technology, Oxford Limited | Oxford, England | United Kingdom | about 21 hours ago
the macroeconomic case for preventative health Developing health metrics using large datasets and causal inference techniques Theoretical models assessing welfare gains and resource allocation in health Economic
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application of conditional diffusion models, flow matching techniques, or related generative approaches, as well as experience working with probabilistic (Bayesian) methods and statistical modelling. Strong
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well as experience working with probabilistic (Bayesian) methods and statistical modelling. Strong writing and programming skills are essential. A proven record of publishing in high-quality journals and presenting
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Survey of Space and Time (LSST) and the Simons Observatory (SO), two surveys the JBCA is heavily involved in. One post will be centered on developing simulation-based inference methods for the joint
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or pathway inference tools Experience working in high-performance computing or cloud environments Interest in developing novel computational or statistical methods for muscle biology Enthusiasm for open
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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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frameworks and pipelines Familiarity with single-cell multi-omic data integration and network or pathway inference tools Experience working in high-performance computing or cloud environments Interest in
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and frameworks we work on, and opportunities for applying the methods with top-notch collaborators. Your work will develop algorithms, inference methods, and frameworks to adapt models from training