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Field
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to the lack of accurate models, machine learning-based problem solving is now revolutionizing almost every field of science and technology. FuturoChrom aims at developing model-free, purely reinforcement
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, and rigorously evaluate machine learning and deep learning models (CNNs, DNNs, transformers, graph neural networks, diffusion models, multimodal models, reinforcement learning) as well as software
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challenging real-world tasks. They will also explore reinforcement learning strategies to optimize decision-making policies in complex environments, and develop fine-tuning protocols for large pre-trained
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on the combination of Reinforcement Learning (RL) and Model Predictive Control (MPC). It will build up upon the work done at ITK on the topic. Several research focuses are considered: verification pathways in RLMPC
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reinforcement learning Enhancing transparency and contestability of decision-making processes, taking a multimodal approach to reveal the reasoning behind complex AI-driven planning and learning algorithms
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PhD in Computer Science, AI, Machine Learning or related field Experience Strong track record of publications in top-tier venues (e.g. CORE A*) Expertise in reinforcement learning, AI agents, and LLM
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learning and computer simulations. The focus of the PhD project will lie on developing machine learning models for clustering, classification, regression and reinforcement tasks to work with, enhance
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. Please do not contact us for unsolicited services. Where to apply Website https://www.academictransfer.com/en/jobs/360266/phd-position-probabilistic-infe… Requirements Additional Information Website
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and Delft University of Technology. Curious to learn more about the project? Feel free to visit our website , where you’ll also find other exciting PhD opportunities related to this collaboration. In
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) and Reinforcement Learning (RL) to acquire manipulation skills and conduct dexterous grasping. PhD candidate will explore the use of Large Language Models (LLMs) to guide task understanding, planning