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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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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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, epigenomics and transcriptomics from these models alongside extensive human tumour collections. You will be PhD-qualified, or have submitted your PhD thesis for examination at the point of commencing this post
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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
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needs. While muscle imaging from well-characterised patients and transcriptomic technologies provide rich data, these remain under-utilised for predictive modelling. Using machine learning, this project
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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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Application deadline: 15/08/2025 Research theme: Computer Science No. of positions: 1 Eligible for: UK This 4-year PhD project will be funded by DLA studentship and is open to UK students
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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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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