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National Energy Technology Laboratory (NETL) | Morgantown, West Virginia | United States | about 2 months ago
permeability of fluid flow through hydrate-bearing sediments, • High resolution visualizations of hydrate distributions and fluid migration in porous media under in situ conditions, and • Machine learning
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National Energy Technology Laboratory (NETL) | Morgantown, West Virginia | United States | about 21 hours ago
on geomechanical stress-strain measurements, • Water conductivity and relative permeability of fluid flow through hydrate-bearing sediments, • High resolution visualizations of hydrate distributions and fluid
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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(30%) Assist in the design, planning, scheduling, and coordinating laboratory and field experimental works in support of various research projects (7%) Program coding, machine learning algorithms
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efficient transmission and robust communication. This work involves formulating rigorous analytical models, designing efficient algorithms, and establishing performance characterization methods that drive
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algorithms and validate findings through computational studies, with the goal of publishing in leading operations research and management science journals. Job Requirements: Candidates must hold a Bachelor in
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with a high pulse power device. Another study intends to develop non-thermal electrons distribution which is of trendemous importance for UHI experiments. Durée du contrat (en mois) 2 ans + 2 x 1an
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the intersection between quantum information and particle physics. The successful applicant will work in Professor Carlos Argüelles’ group on the development of new algorithms to encode and process particle physics
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. - Applied Mathematics and Statistics: Knowledge of statistical methods and algorithms for data analysis, including model fitting, regression, and sensitivity analysis. Expertise - Scientific Collaboration
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the theory of brain-inspired algorithms and apply them to complex, real-world problems. The successful candidate will join an interdisciplinary team of computer scientists, mathematicians, engineers, and