173 machine-learning "https:" "https:" "https:" "https:" "https:" "University of St" positions at Oak Ridge National Laboratory
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
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
-
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
-
multiple types of sensing modalities, where this expertise is applied to solve critical national problems in energy and security. Demonstrated knowledge of emerging AI and machine learning techniques as
-
Postdoctoral Research Associate - Theory-in-the-loop of Autonomous Experiments for Materials-by-Desi
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
-
of quality initiatives, assesses satisfaction, and exchanges feedback and lessons learned. Analyze, interpret, and communicate quality and performance data to management in support of established metrics and
-
, and compliance requirements. Strong aptitude for computer systems, electronic tools, and digital workflows. Ability to learn and adapt to new technologies, including AI-enabled tools used to support
-
, 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
-
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
-
) 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
-
, substation, corridor scenarios) Integrate physics-informed machine learning models with signal processing feature extraction Develop prototype software tools for automated waveform analytics and real-time