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manufacturing sectors, from SMEs to large global manufacturers. For details, visit the MTC website . Entry requirements: A 1st or high 2:1 degree in computer science, manufacturing/industrial engineering, data
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Job Description Are you passionate about environmental contaminants, food safety, marine ecosystems, and creating real-world impact through cutting-edge analytical and data-driven approaches? Do you
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skills, which will be required to lead the development of automated data analysis and modelling. The student will also undertake a research secondment to coral reef monitoring sites at NEOM for three
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. Information session We are inviting you to attend an online information session about LUMS PhD programmes on 13th January 2026, 12:00-13:30 (GMT). Prospective students will obtain information about our PhD
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Are you passionate about artificial intelligence, data analytics, and cutting-edge engineering applications? Join our interdisciplinary research team and contribute to the future of Non-Destructive
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PhD Research Fellow in Data Integrity for Digital Health & Medical Wearables (DC9) at the University of Ulm, Germany (ENDOTRAIN) PhD-position at the University of Ulm, Germany (ENDOTRAIN) (DC9
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15th February 2026 Languages English English English PhD Research Fellow in Data Integrity for Digital Health & Medical Wearables (DC9) at the University of Ulm, Germany (ENDOTRAIN) PhD-position
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with another PhD student at UCLouvain. The PhD candidate will carry out academic research in the fields of urban mobility and foresight. In particular, he/she will: collect and analyze qualitative data
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can be undertaken on either a full-time or part-time basis and is available only to a candidate who is eligible for UK home fee status. Start date 1 October 2026 For further information and how to apply
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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