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collaborative and interdisciplinary activities. Job Requirements: Preferably PhD degree in Computer Engineering, Computer Science, Applied Mathematics or equivalent. Strong academic background in machine learning
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(DFT) simulations and develop machine learning potentials to investigate zeolite-related systems. The role will focus on delivering research projects and promoting research excellence in this area. The
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road safety analytics framework. The role involves integrating multi-source transport datasets, developing advanced analytical and machine learning models for risk identification, and supporting
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may include, but are not limited to: Artificial Intelligence and Machine Learning in Art History Museums, Collections and AI 3D Scanning and Digital Heritage Extended Reality (VR, AR and Mixed Reality
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ideal for a researcher with a passion for solving complex problems at the intersection of wireless communications, edge computing, and machine learning, and who is eager to translate theoretical insights
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Python and/or C++. Demonstrable experience with machine learning frameworks (e.g., PyTorch, TensorFlow). Hands-on experience with game AI agents and/or GUI agents such as Mineflayer, Unity ML-Agents
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, Electrical Engineering, or a related discipline. Strong research background in one or more of: Computer Vision Machine Learning Deep Learning Video Understanding Multimodal AI Excellent programming skills in
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develop statistical, mathematical, causal-inference, simulation, and machine-learning methods using large-scale flight trajectory and operational datasets. Research may cover airport surface congestion
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for: Conducting research and development on AI-based solutions for automated defect inspection and condition assessment of train components by designing and developing deep learning, computer vision, and machine
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demonstrate how innovative methodologies can generate new knowledge within art history. Relevant topics may include, but are not limited to: Artificial Intelligence and Machine Learning in Art History Museums