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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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and build grant or fellowship applications. About You Candidates must have a PhD (or close to completion) or research qualification/experience equivalent to PhD level in health data science or similar
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About the Role We invite applications for a Postdoctoral Research Associate to contribute to the “Big Data for Cardio-Oncology” programme funded by the Wellcome Trust. The research leverages
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genome encodes gene expression levels. You will undertake large scale data generation from primary human samples using a method recently pioneered by the host laboratory (Hua et al., Nature 2021 https
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of applying them to data. Collaborative endeavours with members of the IPMU and Oxford groups is highly encouraged. You will have the opportunity to teach. Applicants should have a PhD (or close to completion
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project. The project is part of a large collaborative research effort involving Imperial College London, University of Bristol and three other groups at Cambridge. More information about the overarching
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models, assessing their effects on large-scale-structure (LSS) statistics as measured by the power spectrum and bispectrum of galaxies or intensity maps. The project emphasizes spectroscopic galaxy surveys
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primary research areas: 1) statistical inference in high-dimensional and large-scale testing scenarios; 2) the development of novel model architectures for large-scale proteomics data; and 3) causal
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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
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of atomic environments Detection of extrapolation and low-reference data regimes Active learning in configurational and chemical space Training and benchmarking of large-scale foundational MLFF models More