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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 14 days ago
engineering (within the past 3 years) with a strong background in particle-based (kinetic) and fluid (continuum) methods for the modeling and simulation (M&S) of dense and rarefied ionized gas flows. Candidates
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of engineering, data science and industry collaboration, and is driven by the opportunity to create real-world solutions to complex energy challenges. You will lead modelling, optimisation and feasibility studies
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environment thanks to its state-of-the-art infrastructure. With an innovative approach, UM6P places research and innovation at the heart of its educational project as a driving force of a business model. In its
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, distributed control, formation control, perception, and decision-making in complex environments. Design and implement data-driven approaches, including machine learning, reinforcement learning, surrogate models
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at local and international level, collaborating with AI researchers, engineers and pathologists, to develop and validate your models. Join the nascent Computational Pathology Group Barcelona and
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in AI-driven materials discovery, machine learning applications for materials, or generative AI related to materials. The successful candidate will independently lead a project focused on developing
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Use real-world vessel data, agent-based modelling and digital twin technology to accelerate the transition toward zero-emission inland shipping. Job description The postdoctoral research is part of
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guidance and navigation, data-driven modelling of spacecraft and environmental dynamics, mission planning and operations, and on-board learning in resource-constrained environments. You are strongly
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expertise. Are scientifically curious, independently driven, and motivated by biologically meaningful modelling problems. Have good teaching abilities. Have awareness of diversity and equal opportunity issues
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National Energy Technology Laboratory (NETL) | Pittsburgh, Pennsylvania | United States | about 3 hours ago
, seismic, and completion data. Integrate machine-learning and data-driven techniques for reservoir proxy modeling. Additionally, you will have the opportunity to: Collaborate with cross-disciplinary teams