199 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:" positions at Oak Ridge National Laboratory
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Generative AI and Machine Learning skills. This position resides in the AI Operations Program office within the Application Development Division of the Information Technology Services Directorate. Our AI/ML
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-body ab-initio methods for description of electronic, magnetic, and vibrational properties in a range of materials Expertise with artificial intelligence and machine learning approaches will be also
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Requisition Id 17081 Overview: We are seeking a Beam Instrumentation Physicist to support the design, development, implementation, and maintenance of beam instrumentation computer systems
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export control laws and regulations. Identify high risk property, including items that are proliferation-sensitive, export controlled, military/weapons related, or specially designed or prepared
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computational thermodynamic (CALPHAD) software, such as Thermo-Calc, DICTRA, PANDAT, or FactSage. Proficiency in materials data analytics, including correlation analysis and machine learning techniques. Preferred
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priorities and dependencies across projects; and ensures key deliverables are achieved safely, on schedule, within scope, and within budget. The position directs knowledge-transfer and organizational-learning
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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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professionalism in a demanding environment. Excellent organizational and work planning skills; able to prioritize and handle multiple tasks. Computer proficiency in Windows-based application software and web-based
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software Experience with hot cell operations and/or other remote handling operations using remote machines, cameras, long handled tools, etc. Experience with radioactive environments and radioactive
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in multiscale and multifidelity simulation techniques (ab initio methods at different fidelity, machine learning tight-binding, machine learning force fields, phase-field modeling, and/or kinetic monte