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Field
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research skills and networks among your peers and research academics. Entry requirements: Applicants should have, or expect to achieve, at least a 2:1 Honours degree (or equivalent) in a chemistry related
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The successful candidate will work in a highly interdisciplinary environment as part of a vibrant cohort of HDR UK-funded PhD students, with outstanding networking opportunities with fellow students
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collection of evidence of impact through communication activities, feature-limited software releases and efforts to inform policy. Candidates for this PhD should have a strong foundation in computer
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conferences, expanding your network and gaining valuable experience in scientific communication. Supervisors: Dr Isabella Guido and Dr Richard Sear Entry requirements Open to UK nationals only. Starting in
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developed a unique methodology and software to simulate and analyse the performance of gas turbine engines in the past half century. The research in this area at Cranfield will be a good starting point for
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cancer using graph neural networks. Our current efforts extend this to additional cancers and modalities, such as multiplexed immunohistochemistry (mIHC), immunoflouresence, spatial transcriptomics and
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development over the last two decades. This research topic aims to define novel approaches to developing and combining these intelligences, utilizing both 1st and 2nd wave AI approaches, in the context
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systems can be self-powered. By optimising energy efficiency, waste from disposable batteries and the need for cabling can be reduced, which supports the global Net Zero goals in decarbonisation with
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and training activities that will contribute to developing professional networks. Understanding the growth of surface oxide films on/in multi-component metallic substrates in electrolyte media is of
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will then embark on coming up with a suitable design for a generic economic model and construct the model using their software of choice. This new generic model can then be demonstrated by using data