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an opportunity to gain experience in large-scale primary data collection, community-engaged research, and advanced longitudinal and mixed-method analyses. The fellow will be a key member of an interdisciplinary
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Postdoctoral Fellow in Kagome Lattice and van der Waals based Quantum Materials Your tasks Conduct in-house and large-scale facility experiments Write beam-time application proposals Analyse scientific data and
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artificial intelligence methodologies. The successful candidate will work at the forefront of computational biology, developing novel approaches for large-scale genomic data analysis and contributing
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Research Fellow in AI for Healthcare (1 FTE) – ArcHub ICUSafeNotes-School of Computer Science 011287
of other medical data, to support clinical decision making. This builds on our prior research in which we have assessing the usefulness of a large language model to query and summarize unstructured medical
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the process of measuring and analyzing high-throughput, multi-dimensional omics data integrating single cell transcriptomics and spatial proteomics of immune cells from CSF and blood as well as human brain
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to or even much larger than the total sample size n. We refer to such “large p, small n” data as high-dimensional data. An enduring interest in statistics is to compare the covariance matrices of two high
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economic, social and cultural benefits. For more information please visit: https://www.brunel.ac.uk/about/our-history/home The Department of Computer Science at Brunel where this project will be conducted
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environmental data Processing and analyzing large-scale remote sensing datasets from UAV, satellite, and ground-based sensors Leveraging artificial intelligence, e.g. machine learning, reinforcement learning
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largest academic research institute in Europe specialising in all aspects of ICT (Information and Communication Technologies). It is home to over 200 researchers with expertise in all communication and
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at least one programming language. Preferred Qualifications: Experience with numerical modeling and/or data analytics with big data. Broad knowledge of hydrology. Experience in ML applications. Background