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Field
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machine learning (ML) weather and climate models to improve physics-based models, while using physical understanding to enhance ML approaches. By integrating ideas from numerical weather prediction, climate
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Physics, Particle Physics, or Quantum Field Theory; experience in the use of numerical and symbolic algebra methods. Desirable requirements: Experience in nonperturbative methods of Quantum Field Theory
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 4 hours ago
programming (Python, MATLAB, IDL) with experience in data analysis, visualization, optical raytracing, and simulation software. Ability to develop custom analysis tools. Knowledge of numerical methods
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located in Fayetteville, a welcoming community ranked as one of the best places to live in the U.S. The growing region surrounding Fayetteville is home to numerous Fortune 500 companies and one
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Requirements Applicants must hold a doctoral degree in Mathematics, Statistics, Data Science, Artificial Intelligence or a related field.Basic knowledge of numerical methods for ordinary and partial differential
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researcher and to create the opportunity of further development. Detailed description of the work duties, such as: Develop and implement GPU-accelerated thermal runaway models and numerical methods in OpenFOAM
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, or on the observation and monitoring of science and technology or system of higher education, in Portugal or abroad. 6.2 — The score obtained in the curricular evaluation method is expressed on a numeric scale of 0 to 20
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minimum of one year eligibility remaining. Strong skills in technical programming languages, e.g., Matlab, Python, or C++; in computational statistics, i.e., unsupervised and supervised ML methods, incl
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Postdoc (f/m/d) at Rossendorf Beamline at the ESRF / Completed PhD in Physics, Chemistry, Materia...
Helmholtz Association of German Research Centres | Tremblay en France, le de France | France | 2 months ago). The position is part of the EU-funded MaLaR project, which focuses on advancing the understanding of lanthanide and actinide materials and developing synchrotron-based methods for selective rare-earth recovery
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and machine-learning methods for rapid surrogate modeling, model calibration, state estimation, uncertainty quantification, and physics-informed prediction. Contribute to U.S. Department of Energy