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contribute to PhD supervision by providing a placement opportunity. Please contact Prof. Andy Clarkson (email: [email protected] ) for further details. Entry Requirements Applicants should have a first class
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Uncertainty quantification for machine learning models of chemical reactivity In this PhD project, we will develop and implement approaches for estimating the uncertainty in AI predictions
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View All Vacancies Chemistry Location: UK Other Closing Date: Sunday 12 May 2024 Reference: SCI266 Uncertainty quantification for machine learning models of chemical reactivity In this PhD
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PhD Supervisor: Prof Andy Clarkson Supervisory Team: Prof Andy Clarkson and Prof Johan Nilsson Project description: The project is to contribute to a major Ministry of Defence (MoD) research
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under the supervision of Dr Lily Asquith. The NOvA experiment uses the world’s most powerful neutrino beam provided by Fermilab in Chicago, USA to measure the oscillation of muon-type to electron-type
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theoretical, methodological, and applied statistics. A demonstrable ability to produce academic writing of the highest publishable quality is essential. If you have not yet been awarded your PhD but are near
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to address some of the challenges in material performance and device fabrication in organic electronic devices. It makes use of the ability of liquid crystals to spontaneously self-assemble into one and two
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stimulator system. Background to the Project Our ability to interface with the central nervous system is currently constrained by the absence of minimally invasive tools that can provide adequate access
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learning. They are the engine behind the success of state-of-the-art AI solutions, such as ChatGPT and Midjourney applied to natural language processing and computer vision applications, respectively
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/clinical need proposed by our clinical collaborators. You will be working on genomic, molecular (transcriptomics, proteomic, and metabolomics), and electronic health record data, empowered by and