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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 3 hours ago
to): Develop machine learning algorithms that utilize fire products from geostationary satellites to better represent fire evolution and variability Develop machine learning emulators to represent forward
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leading role in developing and implementing predictive algorithms designed to identify those most at risk from extreme heat, as well as offering personalized adaptation advice --- translating rich multi
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algorithms for mining molecular multi-omics and medical data to improve multiple tasks of precision medicine and discover new precision therapeutics. The successful candidates will work closely with our
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program at SMART. The SMART team seeks to advance the frontier of AI research, apply it to society and cities, and demonstrate the concrete social impacts of the AI algorithms with broad public acceptance
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foundations, focusing on the structure and complexity of quantum computational models; (ii) optimization and related graph-theoretic methods, using polyhedral geometry and algorithmic optimization (e.g., vertex
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out research activities, including reports, studies and scientific articles; Creation of algorithms, code snippets and development of statistical methods to achieve the desired results; Collaboration
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of statistical algorithms. Duties include undertaking your own programme of research and assisting with ongoing research programmes, using databases, modelling, and computation as appropriate; writing up results
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-class S&T products for sensitive national security missions. The selected candidate will support research efforts in signal processing and analysis, with an emphasis on the development of novel algorithms
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will help design and build novel systems and algorithms that challenge traditional assumptions in databases, guided by both real-world needs and formal foundations. They will work at the intersection
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, and protein structure prediction by artificial intelligence algorithms. The goal is to generate functional models of multimeric protein complexes and how they assemble as a guide to understand disease