63 phd-computer-artificial-machine-human research jobs at Oak Ridge National Laboratory
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Requisition Id 16262 Overview: We are seeking a postdoctoral researcher to work at the intersection of tensor networks, quantum algorithms, scientific computing, topological physics, and quantum
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applied mathematics and computer science, experimental computing systems, scalable algorithms and systems, artificial intelligence and machine learning, data management, workflow systems, analysis and
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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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-flows. Basic Qualifications: PhD in mathematics, computer science, engineering, or related field earned within the last 5 years. Preferred Qualifications: Experience with mesh generation/CFD applications
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respectful workplace – in how we treat one another, work together, and measure success. Required Qualifications: PhD in Nuclear engineering, computer science, applied mathematics, or a related field completed
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capabilities in a wide range of areas, including applied mathematics and computer science, experimental computing systems, scalable algorithms and systems, artificial intelligence and machine learning, data
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applied mathematics and computer science, experimental computing systems, scalable algorithms and systems, artificial intelligence and machine learning, data management, workflow systems, analysis and
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in ORNL’s Center for Radiation Protection Knowledge (CRPK). The candidate will work with experts in computational radiation dosimetry and risk assessment. The candidate should be an independent thinker
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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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to Computational Fluid Dynamics. Mathematical topics of interest include structure-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and