659 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"IFM" positions at The Ohio State University
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students in a higher education learning environment. PHYSICAL CONDITIONS: Sitting/staring at computer screens for long periods of time. Exposure to animal dander and/or zoonotic diseases. Navigating
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with eight (8) years of hands‑on experience in automation and AI, including expertise with Microsoft Foundry, large language models (LLMs), and machine learning technologies. Additional Information: Our
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, coordination of multiple projects, work in Computer Aided Design (CAD) technology, Project Management software (Trimble Unity Construct) and understand regulatory and ADA compliances. Position Summary
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secure storage. Learn more at osc.edu/explore. OSC is a welcoming and supportive work environment and part of the Ohio Technology Consortium (OH-TECH). As the technology and information division
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educational opportunities. Completes annual and other mandatory competencies by either computer or hands on learning by required deadline. Shares professional/technical expertise with students on clinical
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, flexible, and very adaptable to change because the payer rules change constantly. The CFCM-RN must be able to troubleshoot, problem solve, continuously learn, be highly independent, self-motivated and have
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applicable trade specific codes, standards and regulations. 5. Learn technical skills and apply new knowledge to solve problems. 6. Select and use hand, power and machine tools. 7. Establish and maintain
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Interaction or related fields. 4-8 years of relevant experience preferred. 4-10 years of professional experience in instructional/learning design or related field desired. Experience designing and implementing
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. Computer Skills: Requires thorough working knowledge of Microsoft Office applications, TECHSYS and Censitrac tracking and be capable of learning software programs and/or hardware as required. Certificates
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-time, hourly Analyst for a role that involves a large breast cancer dataset. The role includes data cleaning for a large, medical dataset of MRIs and machine learning analysis of time-series data