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Field
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cross-disciplinary research initiative involving both computer and material scientists, providing excellent opportunities for practical impact by taking the outputs from the developed machine learning
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collection activities. Supervision will be provided by academics from various disciplines specializing in biomechanics, image processing, and computer vision, alongside orthopaedic surgeons and academics.
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, for instance, utilise conversational agents, computer vision, mixed reality, wearables etc. Disability, Technology, and Society: Research with a sociological or anthropological focus on the use of bespoke and/or
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equivalent) in computer science, data science, applied mathematics, physics, materials science, or a related field Prior experience in computer vision, deep learning, or signal processing; familiarity with
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PhD scholarship in Runtime Multimodal Multiplayer Virtual Learning Environment (VLE) - DTU Construct
» Mechanical engineering Computer science Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Denmark Application Deadline 19 Oct 2025 - 23:59 (Europe/Copenhagen) Type of Contract
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consequences of keel bone deviations: What impact do these have on hen behaviour and wellbeing? high-tech welfare assessment: Help develop a non-invasive computer vision method to track and analyze how hens move
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including predictive modelling, computer vision and epidemiology. The student will join an established team of investigators, including statisticians, epidemiologists, image scientists, and clinicians
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partners across Europe to deliver a world-class doctoral training programme in risk assessment, resilience engineering, and smart technologies. Its scientific vision targets: (1) the development of a
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Netherlands Application Deadline 3 Oct 2025 - 21:59 (UTC) Type of Contract Temporary Job Status Not Applicable Hours Per Week 40.0 Is the job funded through the EU Research Framework Programme? Not funded by a
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"Digital Substation". Digital Substation opens up the possibility for use of AI along with Edge Computing on large amounts of high-resolution time-series data. In order to take advantage of the full