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Fellow with a PhD in Computational Biology, Bioinformatics, Biostatistics, Data Science, or a related quantitative field to join an interdisciplinary research program focused on Alzheimer’s disease (AD
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on research pertinent to the project. By scaling up data, compute and model size, large language models (LLMs) have gained an impressive and ever growing array of capabilities. The next phase of development
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time of appointment. Demonstrated expertise in bioinformatics, statistics, microbiome data analysis, and/or computational methods. Experience working with large population-level datasets (e.g., NHANES
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perturbation prediction, including the design and implementation of novel training strategies under experimental constraints, e.g., active learning and other data-efficient approachesConduct large-scale
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developing next-generation AI methods for healthy climate adaptation. The position will focus on building and evaluating foundation models for large-scale spatiotemporal health and environmental data. Our team
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Postdoc in pharmacoepidemiology: Long-term safety and benefits of ADHD medication in children and...
. Core research tasks include planning, conducting and publishing epidemiological studies using large-scale observational data, primarily register-based, with a focus on effects of ADHD medication in both
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for 3 years and is based in the Big Data Institute, Old Road Campus, Oxford. The post is only available as full-time. However, we can discuss flexible working hours (e.g. starting and finishing earlier in
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research in focused ultrasound therapy, aiming to achieve breakthroughs in acoustic physics, ultrasound biology, multimodal imaging, intelligent treatment, and clinical big data. Our mission is to develop
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. Job Description: Lead design, deployment, and recovery of a compact multi-station seismic array across the beach–dune transition; manage instrumentation, power, and data logistics. Build data pipelines
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
modeling of large-scale dynamics in networks. This role involves creating large scale models of dynamic phenomena in electrical power networks and quantifying the risk of rare events to mitigate