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disease dynamics, preferably with applications to emerging or re-emerging diseases; Proficiency in programming languages such as R, Python, MATLAB, C++, or similar tools for data analysis and model
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candidate with expertise in the following four areas: (1) working with large-scale digital trace data; (2) building and running natural language processing and machine learning workflows; (3) experimental
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focused on developing and applying methods for estimating the causal treatment effects on health outcomes using large health data. The Mission of the P-HOPER Center is to conduct innovative population
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combination of observational data, machine learning techniques, and cosmological simulations. The group is actively involved in multiple JWST Guaranteed Time Observation (GTO) and General Observer (GO) programs
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Leinweber Institute for Theoretical Physics (LITP). Potential research topics include: inflationary cosmology and its connection to late-time observables; large-scale structure theory and analysis; cosmic
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with computational scientists to integrate and interpret large-scale genomics data. Present research findings in lab meetings, departmental seminars, and conferences; prepare manuscripts and fellowships
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for longitudinal data analysis or large language models. Strong motivation in the development and application of digital twins, particularly in health and biomedical contexts. Strong computer programming skills
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at the field, drone, and satellite scale. Big data analysis including processing and analyzing EMIT hyperspectral datacubes over large spatial domains such as the Colorado Plateau and even global. Presenting
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several areas of ongoing research in the laboratory, including the isolation and genomic analysis of large DNA viruses, cultivation of diverse hosts, analysis of evolutionary relationships and genomic
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-based models, and remote sensing technologies. Required Qualifications • Ph.D. in Civil or Environmental Engineering, Hydrology, Data Science, Geosciences, Computer Science, or a related field. PhD must