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solutions for large-scale scientific data models in federated learning environments. You will advance privacy-preserving machine learning by developing efficient techniques that maintain robust privacy
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
-learning interatomic potentials (MLIPs) and phase field modeling, particularly related to materials for next-generation microelectronics (e.g. oxide ferroelectrics, 2D materials and related systems). Strong
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Expertise in machine learning and big data analysis Excellent written and oral communication skills Motivated self-starter with the ability to work independently and to participate creatively in collaborative
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for simulating atomic nuclei, as well as preparing data and using machine learning models for investigating how the properties of atomic nuclei connect to fundamental questions in physics, such as constraining
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, dimensionality reduction, embeddings, etc.). Understanding of computational scaling techniques for machine learning and high-performance computing. Preferred Qualifications: Expertise in foundational models and
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. Implement and optimize data representations and pipelines suitable for machine learning and uncertainty quantification. Collaborate with AI/ML experts to design and test inference methods that map
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Research Associate to develop and apply scalable artificial intelligence (AI) / deep learning (DL) methods to advance multi-scale coupled physics simulations in support of the missions and programs of the US
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-based modeling of hydrological and Earth system processes. The CHAS group conducts world-class research in hydrological and Earth system modeling, large-scale data analytics and machine learning (ML), and
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thermomechanics. Major Duties/Responsibilities: Help to develop and apply physics-based and/or machine learning models for advanced manufacturing processes. Author peer reviewed papers for journals and conference
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of agentic AI for science, scientific reasoning, federated & collaborative learning, and reinforcement learning (RL) for self-improving models, in the context of leadership scientific workflows and