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Field
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learning (RL) and deep reinforcement learning (DRL) for autonomous process management, dynamic resource distribution, and real-time decision-making. Design and deploy digital twins for integrated chemical
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future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
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processes Implement models and perform numerical simulations Analyze and interpret simulation and experimental data Integrate AI tools and workflows to enhance modeling and accelerate learning Collaborate
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training of the assigned faculty supervisor. This role requires a solid foundation of discipline‑specific knowledge in plant physiology, CT imaging, and deep learning/AI, and the ability to generate
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. ● Demonstrated expertise in causal inference, with interest in methods development. ● Experience with statistical and ML methods, including at least one of the following: Bayesian methods, deep learning
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project that will establish attosecond chiroptical pump-probe spectroscopy. The project combines a 100 kHz few-cycle Yb-fibre laser, circularly polarized isolated attosecond pulses, tuneable 1-2 fs deep
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modern deep learning frameworks (PyTorch, JAX, or equivalent). Have good software engineering habits — modular, well-documented, reproducible code. Are comfortable working in interdisciplinary teams and
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, or a related field. Solid research background and practical experience in one or more of the following areas: Reinforcement Learning / Deep Reinforcement Learning Fine-tuning and Application of Large
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future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
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raises deep and largely unsolved challenges. As a postdoctoral researcher, you will tackle exactly this question: how to generate code with AI and formally verify that it does what it should. Your work