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. • Collaborate with mathematical modelers and experimentalists in the NIH Center to iteratively refine learned models. Qualifications: • Ph.D. in applied mathematics, computational science, statistics, machine
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RAP opportunity at National Institute of Standards and Technology NIST Computational Modeling of Fracture and Fatigue in Hydrogen Environments Location Material Measurement Laboratory, Applied
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16.08.2023, Wissenschaftliches Personal The Chair of Computational Modeling and Simulation (CMS) at the Technical University of Munich invites applications for the position of a Research Assistant
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4 Sep 2025 Job Information Organisation/Company Instituto Pedro Nunes Research Field Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Positions Bachelor Positions
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: reduced-order models (ROMs) and input-output models derived from high-fidelity Computational Fluid Dynamics (CFD) models; data-based models determined from training/calibration data by system/parameter
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motivated and talented computational scientist to join the ice sheet modelling team at the Institute for Marine and Antarctic Studies (IMAS) , University of Tasmania, working closely with Australia’s Climate
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in the areas of Hydrological and Earth System Modeling and Artificial Intelligence (AI). The successful candidate will have a strong background in computational science, data analysis, and process
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on accurate constitutive models that describe the behavior of the molten material during forming. With the increasing demand for more complex components, a step change in model accuracy and associated material
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Koziarski Lab - The Hospital for Sick Children | Central Toronto Roselawn, Ontario | Canada | 4 days ago
program at The Hospital for Sick Children, University of Toronto, and the Vector Institute. Our research group focuses on developing machine learning-based pipelines that leverage generative models and
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/video segmentation, object tracking, reinforcement learning. Deep learning Medical image computing (preferably x-ray imaging) Computationally efficient deep learning Deep learning model generalisation