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Field
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Materials Design - Develop and apply machine learning and AI models (e.g., ML interatomic potentials, generative design, reinforcement learning) to predict and design materials. - Perform first-principles and
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algorithms for cooperative target tracking in complex environments. The role will focus on multi-agent reinforcement learning, decentralized target assignment, occlusion-aware decision-making, and
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proving, potentially including supervised fine-tuning and reinforcement learning using formal verification feedback, and AI agents for proof generation, repair and refactoring. Contribute to the development
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speed, compute efficiency, and scalability with concurrent agents. Enable real-time adaptive learning via human-in-the-loop feedback and reinforcement learning mechanisms in collaboration with other work
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Model Development Support development and evaluation of learning-based control policies using reinforcement learning, imitation learning, and visuomotor learning approaches. Assist in model training
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via human-in-the-loop feedback and reinforcement learning mechanisms in collaboration with other work packages. Maintain high software engineering standards through rigorous testing, version control
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-making methods powered by supervised and reinforcement learning, which aim at trustworthiness in AI-assisted human control with augmented cognition, hybrid human-AI co-learning and autonomous AI, with
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algorithms such as PINN, SKINN. Building agentic economic world models that are incorporated with theory and knowledge structures and connecting them with reinforcement learning and generative modelling
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intelligence algorithms using Python. Knowledge of machine learning or reinforcement learning techniques is highly advantageous. Experience with theoretical wireless network modelling, particularly stochastic
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. Advanced machine learning, reinforcement learning, and agent-based optimization techniques will be developed to reduce voltage deviations, cut active power curtailment, and improve system adaptability under