404 Design-"https:"-"https:"-"https:"-"https:"-"https:" positions at Oak Ridge National Laboratory
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on-site and off-site subcontractors used to execute waste management and transportation responsibilities. Manage and improve systems and programs designed to effectively manage ORNL materials, waste and
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and provide technical engineering support, technology assessments, engineering analysis, engineering studies, design reviews, and specifications development for upgrading existing radar and optical
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, design reviews, and specifications development for upgrading existing radar and optical tracking systems and developing new radar and optical tracking systems for the Department of Energy (DoE) and other
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that transform how buildings are designed and constructed. The successful candidate will contribute to a UT-ORII Convergent Research Initiative (CRI) aimed at enabling affordable, resilient, high-productivity
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live happy and healthy. Employee amenities such as on-site fitness, banking, and cafeteria facilities are also provided for convenience. Other benefits include the following: Prescription Drug Plan
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cafeteria facilities are also available for added convenience. Other benefits include the following: Prescription Drug Plan, Dental Plan, Vision Plan, 401(k) Retirement Plan, Contributory Pension Plan, Life
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Accountability (NMC&A) requirements. Facilitate or participate in event critiques, causal analyses, and corrective action plan development. Assist with the implementation of Integrated Safety Management (ISM
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, this position is a Workplace Substance Abuse (WSAP) testing designated position. WSAP positions require passing a pre-placement drug test and participation in an ongoing random drug testing program. About ORNL
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requires the ability to obtain and maintain a clearance from the Department of Energy. As such, this position is a Workplace Substance Abuse (WSAP) testing designated position. WSAP positions require passing
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, reinforcement learning, monte-carlo tree-search, causal ML etc. Design, develop, and validate interpretable cross-modal AI/ML models incorporating features from electronic structure theory for predictive