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responsibility for implementing a deep learning work-package as part of a Cancer Research UK-funded programme, developing an image-recognition model to identify morphological features corresponding to clonal
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With the human population estimated to reach 9.8 billion people by 2050, the looming nitrogen (N) crisis, stemming from the intensive use of fertilisers in agriculture, requires urgent global action. Massive synthesis and application of fertiliser N has a large energy cost, causes CO2 release...
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responsibility for implementing a deep learning work-package as part of a Cancer Research UK-funded programme, developing an image-recognition model to identify morphological features corresponding to clonal
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informal event – you can drop in at any time. Background To create and contribute to the creation of knowledge by undertaking a specified range of activities within an established research programme and/or
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Leedham (colorectal cancer biology), Dan Woodcock (cancer genomics), Helen Byrne (mathematical modelling), and Jens Rittscher (computational pathology and imaging AI), offering a unique opportunity to work
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the UKRI through the Frontier Guarantee Programme to Dr Jani R Bolla. The work is to be conducted in his lab in the Department Biology, University of Oxford, South Parks Road, Oxford, OX1 3RB
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. A background in neurobiology, anatomy and a very strong quantitative preparation (with extensive experience in bioinformatics/computer science) will be essential. Prior experience with Drosophila
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principles of machine drives and converters. You will also have opportunities to contribute to our open-source computational tools, teach master-level courses, and advise doctoral students. Our strong industry
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using in vivo models. The role will also include supporting the general program of research within the pre-clinical team. You will work in Containment level 2 and 3 facilities to assist with murine
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related to gravitational wave astronomy. The primary aim will be the development of advanced approaches for computational Bayesian Inference to measure the properties of Compact Binary Coalescence signals