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/ML methods for large astronomical datasets -Human-AI collaboration frameworks for scientific discovery -Large-scale applications to observational data from current and future surveys Access state
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of dynamical systems, which will be integrated into large-scale optimization frameworks to enhance the efficiency and reliability of power grid operations. The Postdoctoral Appointee will be responsible
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technologies, including big data analytics, artificial intelligence (AI), remote data transfer, and cloud computing to help solve complex agricultural problems. -The candidate will work on the synthesis
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POSTDOCTORAL COMPUTATIONAL BIOLOGIST FELLOW, under the Collins Genomics Lab to lead pioneering studies of genetic risk for cancer based on large-scale genome sequencing datasets towards the ultimate goal
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-based modeling of hydrological and Earth system processes. The CHAS group conducts world-class research in hydrological and Earth system modeling, large-scale data analytics and machine learning (ML), and
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analysis, power analysis, mixture models, causal inference, psychometrics, sequential and time series analysis, and/or analyzing large datasets are plusses. Strong data management skills are required
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. Prepares and edits research papers, proposal, and reports. Performs advanced testing procedures and analyses of data. May prepare samples for testing. Performs advanced testing procedure and performs
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of the tremendous variability in landscape, fire, and treatment characteristics to conduct a set of natural experiments, taking an approach based on “big data” and remote sensing to learning from past fuel treatment
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observation and/or handling large data sets are encouraged to apply. Prof. Miller in observational time-domain astronomy, focusing on the study of Type Ia supernovae while working to combine observations and
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one or more of the following areas is a BIG PLUS: data science (machine learning and AI), cancer biology, animal physiology, organic chemistry, E3-ubiquitin biology, and gene editing. In all cases