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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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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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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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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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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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stress, anxiety, depression, and loneliness, and how mental health vulnerabilities increase susceptibility to polarization. Leveraging network science, NLP, behavioral sensing, and causal inference
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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