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Field
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methods of data analytics (e.g., statistics, stochastic analysis, Bayesian statistical analysis), physically-based hydrology and water quality models, and the use of machine learning tools for modeling flow
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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how
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of murine infection models for antifungal efficacy testing as well as for use in in vitro screens. Secondly, to contribute to the development, optimisation and implementation of an automated workflow
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highly valued: multispectral UAV mapping, coastal measurements (ADCP, Kestral, wind, wave, buoy), sediment analysis (particle size) and interpretation; CFD modelling; Irish coastlines and data science. The
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depending on funding. The Oxford Ion Trap Quantum Computing group currently hosts one of the world’s highest performance networked quantum computing demonstrators, capable of remote Bell-pair production
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trials (e.g., diet, FMT), and ex vivo gut models enabling advanced multi-omics analyses of these samples. In addition the lab also maintains a large culture collection, partially linked to genomic data
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 15 hours ago
-powered analytical platforms to support patient-centered biomedical research. Current projects involve building computational frameworks for multi-omics integration, modeling cancer vulnerabilities
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standard cell biology techniques as well as murine in vivo models is required, as well as microscopy and flow cytometry. Experience in molecular biology or structural biology is encouraged but is not
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of Colorado. Fellows will conduct mechanistic and translational research in preclinical models of Chimeric Antigen Receptor (CAR) T cells. Key responsibility is to lead a research project studying CAR T cell
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have the opportunity to develop independent research aligned with the aims of the ADN lab. Current work focuses on machine learning and multivariate decoding of neuroimaging data to predict subjective