143 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:" positions at Oak Ridge National Laboratory
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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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informed of class logistics. Use the Learning Management System (LMS) for monitoring registrations, determining class assignments, setting delivery schedules, printing rosters, and verifying training records
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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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fabrication Machine learning (ML)/artificial intelligence (AI) coursework Experience with AI/ML libraries (TensorFlow, PyTorch) Special Requirements: Work involves various physical requirements and working
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technical leadership in AI security evaluation mechanisms. Required Qualifications Master’s Degree in Computer Science, Computer Engineering, Cybersecurity, or related fields with 7-10 years of experience
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Excellent written and verbal communication skills and an ability and desire for ongoing learning and growth Aptitude for solving problems and developing and implementing solutions Firm grasp of standard
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, or office support experience (or equivalent combination of education and experience). Proficient in office computer systems and software applications such as Microsoft Office/Outlook. Must work well in a team
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needs. By leveraging advanced simulations, machine learning, and data-driven insights, the group enables more effective operations aligned with evolving energy demands. The group also develops hydrologic
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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