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Field
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and student guidance. Assist in the preparation and writing of research grant proposals. Job Requirements: PhD degree in computer engineering, Computer Science, Electronics Engineering or equivalent
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should possess: PhD/Ms/BSc in Computer Science, Artificial Intelligence, Electrical Engineering, or a related discipline. Strong research background in one or more of: Computer Vision Machine Learning Deep
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, research, and professional development a Fellow may participate in include: Learning to advance innovation, technology development, and breakthrough knowledge Learning to facilitate research and multimodal
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, Educational Technology, Learning Sciences, Human-Computer Interaction, or a related discipline. Strong interest in research relating to Generative AI, AI literacy, digital learning, workplace learning, or human
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) and hardware integration. Knowledge of machine learning, reinforcement learning, or vision-language models for robotics is a plus. Hands-on experience with robotic arms (e.g., UR5, Franka Emika
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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
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analysis, extrusion technology, machine learning, and rheology will be advantageous.
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Engineering, Health Informatics, or a related field before starting the position. Strong experience in one or more of the following areas is highly desirable: machine learning, recommendation systems, natural
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designing and building visualization dashboards. Research experience in human-centered AI, or in the integration of AI and machine learning methods into interactive visualization and analysis systems. Strong
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the Electrochemical Engineering Science Group and work within Dr Daniel Niblett's team on the five-year UKRI EPSRC ION-H2 Fellowship. The programme combines computational modelling, machine learning