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techniques) as well as early universe cosmology (cosmic inflation, reheating, neutrinos, baryogenesis, dark matter, cosmic defects, CMB, large scale structures, gravitational waves cosmology, data analysis
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, BLIP), fine-tuning large language models for clinical NLP, and self-supervised contrastive learning—the models will learn to effectively combine visual and textual information. By developing
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Alonso and others at the Beecroft Institute of Particle Astrophysics and Cosmology (BIPAC), on research aimed at extracting cosmological information from large-scale structure (LSS) and Cosmic Microwave
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the following areas: Metabolomics data analysis, mass spectrometric analysis of biological samples, biomarker discovery and Chemical Biology. Those who have obtained a PhD degree three years prior
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with researchers both at Stanford and the U.S. Census Bureau. The position is open to recent graduates of PhD programs in economics, statistics, sociology or related data science fields, preferably with
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of thousands of individuals, large-scale single-cell data from primary human tissues, spatial transcriptomic data, and experimental genomic screens. The postholder will join a multidisciplinary team based in
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information about the role, please click 'Apply' above. Applicants must have a PhD. Appointment at Research Associate level is dependent on having a PhD. Those who have submitted but not yet received their PhD
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multiple imaging modalities—initially concentrating on whole-body and abdominal MRI—using UK Biobank imaging data. About the Role The post is funded for 3 years and is based in the Big Data Institute, Old
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contribute to patents or technical innovations. Qualifications: PhD in Artificial Intelligence, Machine Learning, Data Science, Electrical Engineering, or a related field. Strong experience in developing and
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language processing, historical linguistics, and computational humanities. The postholder will lead the computational stream of the project, which involves building a large corpus of Latin texts (data collection and