130 data-"https:"-"https:"-"https:"-"https:"-"Fraunhofer-Gesellschaft" positions at Loughborough University
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of real-time adaptive 3D inspection, dynamically adjusting its measurement strategy based on data quality as well as environmental and scene cues. Positioned at the intersection of robotics, computer vision
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methods and approaches then consider working with us. Digital futures in design and creative arts interrogate the transformative impact of AI, data, and emerging technologies on practice, society, and
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Research Associate in Information Management for People-led Net Zero Specialist and Supporting Academic Research grade 6 from £35608 to £44746 Wolfson School of Mechanical Electrical and
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develop AI- and deep learning–based computer vision tools to automatically identify and quantify intertidal organisms. Beyond computer vision, it will leverage machine learning for large-scale, data-driven
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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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to missed detections, unstable feature extraction, and reduced confidence in data interpretation. Current perception pipelines treat observations as direct ground truth, yet at sea the visual signal is a
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. The role will involve establishing the optimum operating set-ups/parameters (SOPs) of micro-computed tomography equipment to acquire quality 3D image data sets. These data sets will then be re-constructed
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optical frequency combs, the design of optical benches, the acquisition of data using fast electronics and their analysis. The successful candidate is expected to actively engage in our research plans
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and analysing data derived from analytical techniques. Demonstrate excellent communication and interpersonal skills Have a PhD degree (or close to completion) in a related subject or equivalent
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motivated candidate with a 1st or high 2:1 degree in mechanical engineering, materials science, or a related field. Interest in advanced manufacturing and sustainability is essential. Experimental and data