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for extracting physiological biomarkers from ECG, PPG, and related sensor data Machine learning and AI for predictive modelling and risk stratification Computational physiology modelling to personalise and
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structural molecules. Unravelling the ways in which these motifs are encoded into GAGs by their biosynthetic machinery is the fundamental challenge behind the BBSRC-funded GlycoWeb project. This 4-year PhD
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background in physics, biophysics, biological physics, or bioengineering. This PhD project will primarily focus on experimental research, which will include data analysis and there is scope for modelling
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4-Year PhD Studentship: Deciphering how domain organisation regulates heparan sulphate function Supervisors: Prof Cathy Merry, Prof. Kenton Arkill, Dr Andrew Hook Overview Glycosaminoglycans (GAGs
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This 3.5-year PhD project is fully funded and open to home students. The successful candidate will receive a tax-free stipend set at the UKRI rate (£20,780 for 2025/26) and tuition fees will be paid
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The overall aim of this PhD project is to understand how indoor air quality (IAQ) affects the health of children and adolescents, including their mental health with the aim of creating healthier
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, economic viability, and robustness to realistic operational uncertainty. PhD (or equivalent) in control engineering or closely related discipline. Track record in at least two areas: model predictive control
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motion and the viewing perspective of the observer (Nikolaidis et al, 2016). This project will develop continuous models of action legibility using these sources of information from data collected in a
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with a background in cognitive psychology, data science or computer science and a willingness to develop skills in computational models of cognitive processes, statistical methods, and programming (R
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Qualification Type: PhD Location: Nottingham Funding For: UK Students Funding amount: Full tuition fee waiver pa (Home Students only) and stipend at above UKRI rates pa (currently at £20,780