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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 2 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