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experience; the coherence, importance, and viability of the proposed research; and its relevance to the project; and fit with supervisorial strengths at Durham.
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://flood-cdt.ac.uk . Please note, that your application will be assessed upon: (1) Motivation and Career Aspirations; (2) Potential & Intellectual Excellence; (3) Suitability for specific project and (4) Fit
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more frequent and intense extreme rainfall events, creating serious challenges for flood risk management across the UK. Current rainfall datasets are not fit for purpose: radar estimates can be
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, and can demonstrate competence in, two or more of the following areas are encouraged to get in touch. Please note that you **do not** have to check all of the boxes, but you should be excited to learn
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experience, candidates are expected to have strong teamwork and collaboration skills, as well as competency in critical and independent thinking. You’re a good fit if: you have experience in machine learning
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& Intellectual Excellence; (3) Suitability for specific project and (4) Fit to FLOOD-CDT. So please familiarise yourselves with FLOOD-CDT before applying. During the application process candidates will need
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) Motivation and Career Aspirations; (2) Potential & Intellectual Excellence; (3) Suitability for specific project and (4) Fit to FLOOD-CDT. So please familiarise yourselves with FLOOD-CDT before applying
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, this will be confirmed via a formal studentship award letter; this will be provided separately from any Offer of Admission and will be subject to standard checks for eligibility and other criteria.
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, we will offer six fully funded PhD studentships each year for the duration of the award – three funded by the AHRC and three match-funded by BCU. This represents a major institutional investment in
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. Demonstrable experience in the use of machine learning potentials. Ability to fit molecular mechanics force fields. A proven track record in developing computer code and software engineering. Excellent written