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, the Peabody Museum, or more technical parts of the University for periods of time to learn about both research and operational workflows. Connections with the Wu Tsai Institute, the AI at Yale program, the Data
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, electrical engineering, robotics, or a related field completed within the last five years. Background in composite material preparation, particularly in fiber reinforced (Glass fiber, silicon carbide fiber
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designing and running large-scale experiments, developing fine-tuning and reinforcement learning approaches, and studying how LLMs can generalize more reliably across tasks and reasoning domains. We
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concentration/functional inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models
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robotics – reinforcement learning, whole-body model predictive control (MPC), and differentiable optimal control – to simulate human balance and step recovery in urban transport scenarios. The goal is a
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Department invites applications for postdoctoral and more senior research positions in two areas of computer science. 1) Quantum computing and quantum architecture and systems. 2) Reinforcement learning
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-based reasoning strategies for oncology, including tool-augmented inference, multi-agent or compound model workflows, process supervision, verifier-guided training, and reinforcement learning-based post
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with deep learning to design van der Waals heterostructures with optimised spin-orbit torque (SOT) efficiency for ultra-low-power memory and computing. Its two pillars are AUTOMATA, an automatic material
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reliability. Develop advanced energy management strategies using artificial intelligence, including reinforcement learning and hybrid classical-quantum approaches. Essential Function Yes Percentage of Time 45