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achieve an average of 65% or higher in your MSc taught assessments to be considered for progression to the PhD studies. Note: you will not graduate with an MSc. If you meet the progression criteria, you
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for household who stay indoors, and to prepare for emergency responses. Possible quantitative methodologies include concurrent time-series analysis of outdoor and indoor environment data, prediction model
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automated rankings. The research includes real-world validation using university admissions data and will contribute to the broader fields of AI in education and ethical decision-making. This PhD research
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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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. The student will perform ‘big data’ analysis of patient cohorts including time-based evaluation of the impact of introducing CT-FFR as a national health intervention into a healthcare system. Exploratory
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for health policy decision-making, these methods will be developed using a Bayesian framework. This PhD project will deliver a substantial contribution to original research in the area of health data science
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partnership with leading industrial partners. Please contact Mohammad Reza Ilkhani ezzmi1@exmail.nottingham.ac.uk for further information and an informal discussion regarding this PhD.
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This fully funded PhD studentship, sponsored by the EPSRC Doctoral Landscape Awards (DLA) and RES Group, offers a bursary of £25,000 per annum, covering full tuition fees. The project focuses
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experience in developing computational models and implementing models for computer simulations. Software development in C++ and/or Python is expected, and experience in model analysis and parameter
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-field methods) Multiscale mechanics and microstructure-property relationships Python/C++/Matlab-based simulation and data analysis Industry-facing research and technology transfer You will also benefit