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analysis, machine learning, and data-driven modeling applied to large astronomical datasets and time-domain surveys is highly desirable. Although experience with microlensing and transit exoplanet searches
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programming skills in modern languages such as Python or C++. Experience in statistical analysis, machine learning, and data-driven modeling applied to large astronomical datasets and time-domain surveys is
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Engineering Department 140401 - Electrical & Computer Engineering Job City & State Raleigh, NC-27606
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research and development spanning areas such as optimization, Fourier analysis, numerical linear algebra, statistics, machine learning, and high-performance computing for one or more of the following: (1
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benchmark for auto-formalization of mathematics, and to engage in related research. Duties will include contributing to the benchmarking dataset, organizing and ensuring the quality of the data contributed by
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computationally intensive field, for example computational social science, computational psychology, or certain areas of digital humanities. 2. Specialist knowledge in NLP or a closely related area of machine
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; Familiarity with machine learning concepts and large language models. Deep expertise is not required, but candidates should be comfortable engaging with these technologies at a foundational level; Knowledge of
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to further our understanding of Earth system processes and to enable environmental management. This includes working with ESS-DIVE users and the broader community to create machine-readable data products and
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Research Associate to develop, scale, and apply artificial intelligence (AI) and deep learning (DL) models for power grid systems. The successful candidate will contribute to scalable AI workflows for grid
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Postdoc (f/m/d) Mechanical Characterization and Materials Assessment for Nuclear Applications / P...
data analysis and statistical methods # Interest in or initial experience with data-driven methods or machine learning is an advantage # Strong communication skills and a collaborative approach to