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computer science and mathematics: Theory of Algorithms and Extremal Combinatorics. By integrating these areas, the project seeks to develop innovative computational techniques and theoretical insights that extend
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, the project will develop machine learning based solutions for predictive grid analytics (such as grid congestion forecast, asset monitoring, etc.). Based on these results, the project will develop
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a Research Engineer to help us develop the next generation of autonomous systems and algorithms for Earth and space applications. Our group doesn't just develop new theories, but puts them to the test
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project bridges two foundational fields in computer science and mathematics: Theory of Algorithms and Extremal Combinatorics. By integrating these areas, the project seeks to develop innovative
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. The Postdoctoral Associate will apply his/her technical skills toward development and implementation of machine learning, computer vision, and other algorithms for analysis of medical images and prognostication as
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data and developing the algorithms and tools that enable the work of their team. Thus, the ideal candidate for this position must have a biological background, the ability to translate scientific
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Technology Environmental Modeling and Decision Support Systems Development, Advanced GeoAI Coding for Algorithm development Scholastic document writing Working towards BS degree in Engineering/Geospatial
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Schedule: Monday – Friday, 8 a.m. – 5 p.m. Summary The Research Associate will be responsible for developing and implementing machine learning algorithms to analyze genomes and predict disease risk in
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algorithm development, data analysis and inference, and image analysis Ability to do original and outstanding research in computational biology, and expertise in computational methods, data analysis, software
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part of the Green Algorithms Initiative in the Department of Public Health and Primary Care, one of Europe's leading academic departments of population health sciences. The post will suit researchers