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Field
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responsibilities include working with communities to understand complexity, collecting and analysing data, and reporting to a range of audiences. About the person: Essential Criteria: An upper second class honours
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technical knowledge and hands-on experience in: Deep learning frameworks (e.g., PyTorch, TensorFlow) Deep learning models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques
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will develop and apply methods to transform omics data into networks and executable models, collaborating closely with experts across the Petsalaki and Sheriff groups, Open Targets, EMBL-EBI, and the
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, solid phase synthesis, and molecular design . We are looking for someone with experience / wish to learn some of the following: complex chemical systems, reaction diffusion, analytical chemistry, chemical
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connectivity of the broader community, training, networking, as well as state-of-the-art research. This post will develop artificial intelligence methods for the prediction of crystal structure, the critical
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a state-of-the-art Earth system models(UKESM). We are looking for someone experienced in analysing output from large, complex climate models or Earth system datasets, and who is experienced in or keen
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-life environments. The Role: As Research Fellow on the COG-MHEAR project, you will have the opportunity to use your strong background in deep neural networks and multimodal hearing-aid signal processing
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.. Contribute to writing up research findings for publication. A strong eye for detail and the ability to undertake tasks systematically and rigorous Co-ordinate the operational aspect of research networks
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ability to employ excellent oral and written communication skills to communicate complex ideas to a range of audiences. You will have experience of teaching and an enthusiasm for teaching undergraduate and
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statistics, with experience of analysing complex clinical trials, with proficiency in one or more statistical software packages (e.g. Stata, SAS, R). Be expected both to support externally funded research