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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | about 6 hours ago
control, and coronagraph system modeling. Location: Ames Research Center Moffet Field, California Field of Science:Planetary Science Advisors: Natasha Batalha natasha.e.batalha@nasa.gov 650-604-2813 Ruslan
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environment. Development of models to diagnose and predict battery performance and ageing. Participation in national and international research projects related with energy storage and its integration in
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such as wrinkles can occur, which cause parts to be scrapped. To minimise material and energy wastage, digital models of the manufacturing processes can be developed and linked to process control and
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. The work is part of the regional project “Optimizing Renewable Energy Integration: FPGA-Based Model Predictive Control (MPC) for Grid Stability” (Ref. SI4/PJI/2024-00238), funded by Comunidad de Madrid and
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on stability. Testing the model in standard stirred tank apparatus Refining the model to allow predictability between different types of apparatus. Defining an algorithm for testing enzyme stability
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to experimentally test predicted models would be appreciated. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UPR2357-PATACH-008/Default.aspx Work Location(s) Number of offers available1Company
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predict the location of resources more accurately, it is necessary to model these processes jointly at the basin scale. However, directly solving geochemical equations is computationally expensive and
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technology. Development of cutting edge foundation models for protein design, small molecule property prediction, or protein function prediction Data generation and curation, including molecular simulation and
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deployment enabling validation and demonstration of real-world applications. For more details, please view https://www.ntu.edu.sg/erian We are looking for a Research Associate to conduct numerical modelling
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, we aim to create autonomous “self-driving” microscopes that: build statistical models of biological dynamics in real time predict the most informative next experiment execute it automatically on living