173 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Mines Paris PSL" positions at Oak Ridge National Laboratory in United States
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Conduct of Research including being personally responsible for ensuring safe operations by raising safety concerns, using a questioning attitude, considering hazards for every task, and never stop learning
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and apply basic GD&T concepts. Experience with ASME piping codes and process piping system design. Experience troubleshooting mechanical or HVAC systems. Understanding of machining and fabrication
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injection techniques, space charge simulation and theory, and control and mitigation of beam halo and other beam loss mechanisms, and machine learning efforts. Provide leadership to the group to support safe
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manufacturing, toolpath generation, machine tool operation, and data analysis. As part of this role, you will be tasked to lead the management of project timelines, troubleshoot equipment for routine maintenance
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enrichment technology related components using 3D Computer Aided Design (CAD) software (SolidWorks). Create 2D drawings to be used for fabrication and communicate with staff and suppliers through
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feedback, and exchange lessons learned. Conduct quality assessments of work, including document reviews, to verify adherence to specifications and compliance with requirements. Serve as software quality
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engineering disciplines and 7+ years of working experience in a related field. Candidates with a Doctoral degree are encouraged to apply as well. Advanced understanding of mechanical systems, machine design
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analytics that enhance and evolve business operations and scientific decision-making capability and related activities at ORNL.Qualified applicants will have a solid foundation of Generative AI and Machine
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Expertise in machine learning and big data analysis Excellent written and oral communication skills Motivated self-starter with the ability to work independently and to participate creatively in collaborative
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of agentic AI for science, scientific reasoning, federated & collaborative learning, and reinforcement learning (RL) for self-improving models, in the context of leadership scientific workflows and