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) for protein analysis and interaction studies Microscopy techniques, including fluorescence microscopy, confocal microscopy, and immunofluorescence imaging Chromatin immunoprecipitation (ChIP) assays to assess
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an understanding of glacioeustatic cyclicity, sequence stratigraphy, fluvial to shelfal marine sedimentology, basin analysis, and multidisciplinary data analysis workflows. Responsibilities Research development and
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power (CSP) systems optimization, computational heat transfer and radiative transport using sophisticated numerical modeling and machine learning approaches for forward and inverse problems in radiation
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multidisciplinary and highly collaborative research team and will lead research projects tailored to the candidate’s interest. The candidate will also have numerous opportunities for career and professional growth
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of very large data sets, data analysis, and simulations of X-ray scattering and spectroscopy signatures of dynamic processes in battery materials. The theoretical/ simulation efforts are supported by
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Sciences and Technology Campus in Roanoke; sites in Newport News and Richmond; and numerous Extension offices and research centers . A leading global research institution, Virginia Tech conducts more than
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evaluation and treatment, social skills groups for girls, craniofacial clinics, developmental disability screenings, applied behavior analysis (ABA), and cleft lip and palate clinics. The program also supports
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performance computing numerical methods in our state-of-the-art open source micromagnetic model, MagTense. MagTense is based on a core implemented in the Fortran programming language, and it relies
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supervision conferring with higher levels only in unusual situations. 8. Performs other job-related duties as required. Responsibilities: -Develop and implement advanced numerical algorithms and deep learning
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data analysis for omics studies, including genotype data, bulk and single-cell RNA sequencing data, high-dimensional phenotype data, pathway and functional analysis, network analysis, polygenic risk