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Field
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is critical for a wide variety of engineering and environmental applications, remains unexplored. You will join a team of researchers that experimentally quantify the flow-physics of fundamental
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requirements for our PhD programme . Skills in Python, C++ or ROS are preferred. How to apply Applications should be submitted via the Robotics and Autonomous Systems PhD programme page. In place of a research
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PhD Studentship: LLM-Based Agentic AI: Foundations, Systems & Applications – PhD (University Funded)
Computer Science or a related field. Strong motivation to conduct groundbreaking AI research that make a positive impact in the real-world. Excellent communication skills in English, both speaking and writing
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The Open University Business and Law Schools are inviting applications to join our campus-based PhD Programme and our new PhD by Distance Learning Programme. The starting date for both programmes is
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marine sciences, biological oceanography, ecology, or computer sciences. Strong analytical, numerical and practical skills are essential. Experience in coding or applying quantitative methods in a
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PhD project (inc. computing, mathematics, engineering etc.). Enthusiasm for research, the ability to think and work independently, excellent analytical skills and strong verbal and written communication
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in mineralogy, geochemistry, material properties and big-picture Earth Science questions. If your first language is not English, you will need to meet the minimum English requirements for the programme
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training programme by the University of Liverpool. As well as gaining a formal HE teaching qualification, you’ll learn key pedagogical skills for the academic job market, primarily through shadowing mentored
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aerospace companies, space agencies, advanced technology companies and academia. You could pursue roles in structural analysis, simulation and certification, data-driven design, or research leadership in
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap