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Field
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learning algorithms and creating predictive models. This role will contribute to evidence-based processes and procedures for program quality review and assessment of outcomes data as well as develop
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calculations Expertise in one or more of the following: computational methods, software and algorithm development, high-performance computing, and data analysis Proficiency in relevant programming languages (e.g
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incumbent will conduct research within the predetermined research scope and methodology, and conduct research in the fields of quantum-engineered power grids and AI-enabled smart grids. ● Develop
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 5 days ago
, and medicine. Key Responsibilities Collaborate with researchers to design, develop, and refine large language and generative models. Develop novel algorithms for generative modeling tasks and optimize
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on developing and analyzing algorithms, building reproducible computational studies, and disseminating results in leading venues. The position collaborates with faculty, students, and external partners
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computational chemistry/molecular physics will support the research program of Dr. Dmitri Babikov: Dr. Dmitri Babikov // Chemistry // Marquette University. The project will focus on the development of a quantum
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to work on Bioinformatics and Computational Biology in Cancer Genomics and Immunology. This position will be involved in the development and/or application of computational approaches to understand
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advanced many-body methods, high-performance computing, and machine learning approaches. The successful candidate will play a leading role in developing computational methods and high-performance algorithms
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 6 hours ago
: Technology Development Advisors: Ryan Rogalin Ryan.Rogalin@jpl.nasa.gov (818) 354-3426 Applications with citizens from Designated Countries will not be accepted at this time, unless they are Legal Permanent
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Designing and extending algorithms grounded in probabilistic machine learning Applying statistical techniques to assess robustness and generalization. Development of methods of research, testing and data