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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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individuals, some tracked for more than a decade. You will be responsible for advancing and applying state-of-the-art probabilistic deep generative models, including conditional diffusion and flow matching
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. presentations, seminars, lab meetings…) as proven by track-record of scientific publications in leading journals and scientific dissemination Demonstrate excellent organisational skills, record keeping, academic
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inference in engineered systems, including telecom networks; The development of neuromorphic algorithms and spiking neural models with built-in efficiency and reliability guarantees; The design of reliability
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-halo connection, and field-level inference (contact: Sownak Bose) Benefits of these roles include: • opportunity to undertake high quality research with connection to impact; • working closely with
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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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independently and as a multi-disciplined team Excellent verbal and written communication skills (e.g. presentations, seminars, lab meetings…) as proven by track-record of scientific publications in leading
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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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-halo connection, and field-level inference (contact: Sownak Bose) Benefits of these roles include: opportunity to undertake high quality research with connection to impact; working closely with people
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Ellison Institute of Technology, Oxford Limited | Oxford, England | United Kingdom | about 19 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