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: Probabilistic generative models (VLMs, diffusion, flow models) Reinforcement learning & Markov decision processes Causal inference & counterfactual reasoning Mechanistic & physics-informed modeling Agentic AI
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communications, IoT, or edge computing. Demonstrated proficiency in software API and algorithm development of edge intelligence algorithms using Python. Knowledge of machine learning or reinforcement learning
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will work across the following research areas: Predictive machine learning Robust and stochastic optimization Learning-enabled control and reinforcement learning Power system operations, planning, and
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data collection with quantitative statistical and modeling analyses and are collaborations with natural resource managers, veterinarians, and academics. You will learn to develop and conduct research
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-time adaptive learning via human-in-the-loop feedback and reinforcement learning mechanisms in collaboration with other work packages. Maintain high software engineering standards through rigorous
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managers, veterinarians, and academics. You will train on several ongoing research projects on wildlife disease around the Greater Yellowstone Ecosystem. Learning Objectives: Through this mentored research
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measurement science research in robotics, advanced autonomy, and artificial intelligence systems. Utilizing deep learning, large language models (LLMs), reinforcement learning, and unsupervised machine learning
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imaging strategies, including reinforcement-learning approaches that dynamically optimize sampling based on environmental conditions such as turbidity and currents Lead the preparation of manuscripts
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success without monitoring how the task is accomplished, users (and especially users in clinical populations) may learn motor strategies that achieve short-term rewards but reinforce harmful patterns
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, operations research or related field. Additional Qualifications Candidates who have a strong mathematical background in reinforcement learning and/or control (e.g., optimal control, decentralized control, and