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the PhD has been awarded. Fixed-term: The funds for this post are available for 18 months in the first instance. To apply online for this vacancy and to view further information about the role, please
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the PhD in Engineering programme. Make sure to reference the supervisor's name (Dr Farzad Hayati) in your application. Informal enquiries are welcome and should be directed to Dr Farzad Hayati at f.hayati
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& robust control, and learning for dynamics & control. The main task of the PhD student will be to develop sound data-driven methodologies for learning control policies with provable guarantees
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Grand Tours, one-day races (monuments and semi-classics), and select World Tour 1-week stage races. The data sources will include video and previous race commentary to ‘code’ key events in races that help
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, or physics, and will demonstrate a willingness for experimental fieldwork and data analysis. This PhD offers a unique opportunity to contribute to frontier research in atmospheric chemistry, with
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. Development of DT information modelling, data fusion, and forecasting guidelines and standards, and technology maturity benchmarks to derive cloud platform maturity level standards. Lead on the development
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sources such as (i) atmospheric models, (ii) satellite remote sensing, (iii) land use information, and (iv) meteorological data. The aim of this PhD is to develop and implement models for integrating data
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the PhD. The Faculty has outstanding facilities and works in partnership with leading industrial partners. Please contact Mohammad Reza Ilkhani ezzmi1@exmail.nottingham.ac.uk for further information and an
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access to bespoke courses developed for Engineering PGRs. including sessions on paper writing, networking and career development after the PhD. The Faculty has outstanding facilities and works in
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ADTKD diagnostics and clinical trials. What you’ll do You will work at the interface of laboratory and data science, and clinical genetics. You will: Establish and characterise the UK ADTKD-MUC1 cohort