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modeling and networked biological systems. You will work at the intersection of high-performance computing (HPC), computational biophysics, and machine learning, leveraging leadership-class computing
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environment. The successful candidate will develop and apply advanced machine learning techniques—including multimodal AI, computer vision, and large language models—to complex scientific and engineering
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Requisition Id 15885 Overview: We are seeking a Postdoctoral Research Associate – Simulation and Machine Learning for Composite Manufacturing who will focus on developing physics-based simulation
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competing structural phases and the vibrational and electronic structure in materials with defects and disorder. This effort will further seek to implement strategies to leverage machine learning techniques
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computational physics, computational materials, and machine learning and artificial intelligence, using the DOE’s leadership class computing facilities. This position will utilize methods such as finite elements
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to numerical methods for kinetic equations. Mathematical topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and
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to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a
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, substation, corridor scenarios) Integrate physics-informed machine learning models with signal processing feature extraction Develop prototype software tools for automated waveform analytics and real-time
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) with questions related to this position. Major Duties/Responsibilities: Develop and apply machine learning models (ML) as surrogates for high-resolution process-based hydrologic models. Design and
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toward integration of hydropower with battery storage and other technologies. Computational and analytical skills : Demonstrated ability in selecting and deploying machine learning tools (Random Forest