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limited supervision, operate a variety of machine tools to inspect, calibrate, or produce precision parts and instruments. You will be responsible for applying knowledge of mechanics, mathematics, metal
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management, workflow management, High Performance Computing (HPC), machine learning and Artificial Intelligence to enhance our capabilities in making AI-ready scientific data. As a postdoctoral fellow at ORNL
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(PMI) Science Focus Area and the GPTgp (Generative Pretrained Transformer for Genomic Photosynthesis) project. This position focuses on developing machine learning pipelines, AI-driven scientific
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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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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