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, the project accelerates trait data acquisition by applying computer vision to herbarium specimens and field photos, as well as large language models to extract complementary information from literature and
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Applications are invited from PhD studentship candidates with good first degrees in computer science, physics, maths, biology, neuroscience, engineering or other relevant disciplines to join
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scanning confocal microscopy and calcium imaging in time-lapse, computational imaging approaches for analysis of images and movie recordings, analysis of the connectome to identify neural circuits
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27 Sep 2025 Job Information Organisation/Company University of Nottingham Research Field Computer science » Other Engineering » Biomedical engineering Medical sciences » Other Researcher Profile
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research. Specific multidisciplinary skills include: synthetic chemistry, structural molecular biology and advanced imaging; it will harness cutting edge computational tools (incl. image analyses), giving
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autophagy and the innate immunity response. Using cutting-edge omics (transcriptomics/proteomics), advanced imaging (confocal) and cellular biology approaches (macrophages, autophagy biomarkers), this project
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We are seeking a research assistant with a background in computing to develop AI models for image reconstruction from data from our ultra-thin fibre-based spatial frequency domain imaging device
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VR/AR, quantum tech, life-sciences, computing and biomedical imaging. The project will work on cutting-edge optical technologies alongside collaborators Prof Melissa Mather, Prof Dmitri Veprintsev, and
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VR/AR, quantum tech, life-sciences, computing and biomedical imaging. The project will work on cutting-edge optical technologies alongside collaborators Prof Melissa Mather, Prof Dmitri Veprintsev, and
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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