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materials using statistical mechanics, molecular simulations, and machine learning. Expectations Candidates will be responsible for: Developing multi-scale modeling methods for polymeric materials, using
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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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machine learning techniques for building efficient reduced-order models in the context of the numerical simulation of parameterized partial differential equations. The analysis of recent deep learning
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Details Posted: Unknown Location: Salary: Summary: Summary here. Details Posted: 10-Apr-26 Location: El Cajon, California Type: Full-time Categories: Academic/Faculty Computer/Information Sciences
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in IEEE Communications Society’s and IEEE Signal Processing Society’s journals and conferences. Strong background in communication theory, signal processing, machine learning, and optimization theory
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diagnosis of gas turbines. The project focuses on developing an integrated approach that combines machine learning techniques with physics-based models to estimate the health of various system components
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, automated reasoning and symbolic AI, and machine learning and neural AI. Applicants should submit their cover letter, research statement, CV, and list of references to positions@icarm.io. Applications will be
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comparable, relevant field of study Understanding of the fundamental concepts and techniques of Artificial Intelligence (AI) and Machine Learning (ML) Knowledge in specific AI application areas such as Natural
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Department of Computer Systems is internationally highly appreciated scientific and learning centre, which scientific competency is focused on creating of technical systems based on computer systems
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computation of the corresponding Gröbner basis becomes almost immediate. We will investigate structural heuristics, exploration of the Gröbner cone, and, more speculatively, machine learning approaches