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development programs. The Office of Fusion Energy Sciences (FES) has four strategic goals: (1) Advance the fundamental science of magnetically confined plasmas to develop the predictive capability needed for a
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Computer Science, Electrical/Computer Engineering, AI/ML, or a closely related field. Demonstrated experience in AI/ML model development, LLM tuning, generative AI, functional safety and risk analysis. Proficiency
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, computer vision in the Division of Health Data Science (HDS) at the DOS. The position is an annually renewable professional academic appointment. Duties/Responsibilities: ● Risk predictive model for clinical
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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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relevant programming languages. Ability to use/learn several advanced modeling methods (e.g., statistical, mathematical, individual-based, or machine learning models). Experience with high-performance
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relevant programming languages. Ability to use/learn several advanced modeling methods (e.g., statistical, mathematical, individual-based, or machine learning models). Experience with high-performance
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 22 hours ago
infectious disease dynamics, epidemic modeling, and/or machine learning and artificial intelligence Preferred Qualifications, Competencies, and Experience * Writing programs and performing analysis in R and/or
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implement MR-based simulation platforms for training and process enhancement in remote construction. Apply machine learning techniques for optimizing construction assembly processes, predictive analytics, and
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, evolutionary biology, computer science, physics, applied mathematics, or engineering. Our research integrates mathematical modeling, machine learning, and quantitative experiments to understand and control
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for design consideration. Irradiated mechanical property prediction models and property correlation metamodels will be developed considering traditional and machine learning approaches. Extrapolation will be