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, modeling and analysis, integrating diverse data sets to identify global risks affecting sourcing strategies. In this role you will: Conduct and contribute to research and model development to enhance
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weather instrumentation data analysis (lidar, radar, weather station, radiometer, sonde, cameras) Experience with mining and cleaning “big” observational data and/or online databases Experience with
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performed by graduate students, and mining existing data sets that will contribute to the body of knowledge in asphalt materials. • Write technical reports that summarize multi-faceted research projects
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will be measured by having published or contributed to papers in top venues (e.g., Nature Science of Learning, Computers and Education, ACM Learning at Scale, Educational Data Mining) and engaging
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moves. Success will be measured by having published or contributed to papers in top venues (e.g., Nature Science of Learning, Computers and Education, ACM Learning at Scale, Educational Data Mining) and
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that integrate simulation, machine learning, and data analysis. Numerical optimization methods (e.g. machine learning including deep neural networks, reinforcement learning, data mining, genetic algorithms
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wetland infill and land reclamation. Data generated will contribute to building the evidence base through generating new empirical knowledge of where sand is being mined and how it moves through
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combination of robotics-assisted experiments and data mining in our 30 years of legacy data regarding the safety, metabolomics, and physiology of the strains. The UPCYFUN project is a unique opportunity for you
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moves. Success will be measured by having published or contributed to papers in top venues (e.g., Nature Science of Learning, Computers and Education, ACM Learning at Scale, Educational Data Mining) and
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are encouraged to apply. Required Qualifications PhD degree in Electrical Engineering or closely related fields. The applicant must have working knowledge of microwave engineering, antennas, noise, heat radiation