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Research Assistant in Laser Material Processing Directorate: School of Engineering & Physical Sciences Grade: Grade 6 (£30,805 - £36,130) pro rata Contract Type: Part Time (0.5FTE), Fixed Term (6
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that combine deep learning, computer vision, and bioinformatics to extract actionable insights from complex, multi-modal data, including medical imaging, genomics, and clinical records. A central theme of our
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research team focused on advancing cancer care through cutting-edge computational and AI technologies. We develop innovative approaches that combine deep learning, computer vision, and bioinformatics
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the combined themes of human-computer interaction and critical computing. The lab will be exploring the notion of "deceptive by design" on all fronts: social identity cues in the design of LLM-based chatbots
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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these bioinformatic experiments. Access to a high-performance computer will be provided. The candidate must be capable of generating complex molecular compound models in silico and using current molecular dynamic
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animal tolerance to liver fluke infection. A range of omics tools are to be used to assess the impact on the sheep host. The PDRA will be focussed on the bioinformatic analyses of these `omics¿ datasets
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animal tolerance to liver fluke infection. A range of omics tools are to be used to assess the impact on the sheep host. The PDRA will be focussed on the bioinformatic analyses of these ‘omics’ datasets
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networks, with a focus on carbon emissions reduction and network telemetry. You will contribute to the development of a framework that reduces the carbon emissions of networks through the use of carbon-aware
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funded by UKRI EPSRC and is fixed term for 12 months. You will be contributing to joint UKRI EPSRC – NSF CBET project on sustainable computer networks, with a focus on carbon emissions reduction and