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project include two aspects: (1) based on the cutting-edge technologies from deep learning, computer vision or physics-informed machine learning, develop robust surrogate forward models to predict
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the division of Data Science and AI , we develop data-driven methods and AI solutions that support intelligent decisions across society, advancing machine learning techniques, from foundations to industrial and
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across the broader TU Delft research community. Job requirements PhD in computer science, AI, machine learning, data science, network science, computational social science, or a related field. Expertise in
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responses are pedagogically valid, contextually appropriate, and consistent with collaborative learning theory. Scaffolding mechanism design. In close collaboration with the PhD candidate and educational
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, ensuring the agent's responses are pedagogically valid, contextually appropriate, and consistent with collaborative learning theory. Scaffolding mechanism design. In close collaboration with the PhD
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interested in mentoring and supporting MSc and PhD students. You are a machine learning enthusiast (and realist). You love coding and have proven experience in e.g. Python, Matlab, JAVA, C#. You can present
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. in Computer Science or a closely related field, with research focus in Reinforcement Learning, Machine Learning, Embodied AI, or AI for Education. Strong background in deep reinforcement learning, with
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to learn about, (road) network traffic flow theory and simulation. You are interested in mentoring and supporting MSc and PhD students. You are a machine learning enthusiast (and realist). You love coding
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the selected person to have the ability and motivation for doctoral studies and to complete a doctoral degree. To be eligible for a full PhD student position, the candidate is required to participate in a
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leverage reinforcement learning, deep learning, and generative AI, and evaluate against the research front in mathematical optimization strategies, to enable efficient, robust, and adaptive evacuation