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Field
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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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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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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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and tradition. Renowned for hands-on learning and pioneering research, Mines educates future leaders in STEM fields who will make a meaningful impact on the world. Our vibrant community of supportive
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processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large
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are enriched from one round to the next, and eventually the library collapses to a few selected functional aptamers. The evolution can be tracked in detail by deep sequencing of the successive rounds. The goal
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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
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(e.g., deep learning, implicit neural representations, diffusion models) for CT reconstruction, enhancement, and defect detection. Advance algorithms for multi-modal tomography (X-ray, neutron, electron
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developing historical and future climate scenarios—including counterfactuals—and integrating them with health surveillance and related data to estimate climate-attributable risk under Deep Uncertainty
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. Required Qualifications: PhD in Computer Science, AI/ML, Computational Biology, or a related quantitative field. Proven expertise in deep generative modeling and large-scale multimodal learning. Experience