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Science, Computer Engineering, Artificial Intelligence, Physics, Mathematical Engineering, Mechanical Engineering or similar. Relevant skills: Strong background in machine learning/data science. Deep knowledge
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environmental changes, agricultural management, and ecosystem sustainability Experience with deep learning, radiative transfer modeling and ecosystem modeling Teaching and supervision experience Who we
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
, robustness, calibration, bias/fairness, and/or adversarial stress-testing. - Solid programming and ML/NLP engineering skills in Python and ideally modern deep-learning stacks (e.g., PyTorch/JAX, HuggingFace
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, Computational Linguistics, Machine learning, Computer Engineering or related fields Preferred Qualifications: ● Strong experience implementing and training deep learning models in PyTorch, with attention
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healthcare. Qualifications Required: PhD (or equivalent) in computer science, statistics, biostatistics, electrical/biomedical engineering, or related quantitative field. Strong background in machine learning
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present research findings at regional and national conferences. Qualifications: A PhD in Chemistry, Materials Science and Engineering, Physics, Nanoscience and Nanoengineering or equivalent is required
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of the appointment. Demonstrated experience with deep learning methods or sophisticated mathematical frameworks applied to large-scale or scientific datasets. Experience working with observational seismology data (e.g
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experience in one or more of the following areas: Reinforcement Learning / Deep Reinforcement Learning Fine-tuning and Application of Large Language Models Time-Series Data Prediction and Modeling Intelligent
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well as condensed matter and materials science. The preferred starting date of this position is July 1, 2026. Qualifications: Candidates should hold a PhD in experimental nuclear or particle physics. Experience with
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in digital health, fitness, and rehabilitation. Preferred Qualifications Background in machine learning/deep learning is a plus. Equipment Utilized Physical Demands and Work Environment PHYSICAL