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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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and galaxy redshift surveys, topics in the galaxy-halo connection, and field-level inference (contact: Sownak Bose) Benefits of these roles include: opportunity to undertake high quality research with