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This PhD project aims to develop and apply innovative photogrammetry-based analytical tools to enhance the spatial and structural characterisation of pharmaceutically relevant materials, with a
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behaviour through these models using uncertainty quantification/machine-learning (UQ/ML) algorithms To optimise the manufacturing process with the help of the simulation tool To support in the development and
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create a working framework that includes both experimental and modelling prototypes, including AI/ML tools to assist with the large number of variables involved. This project is seeking candidates with a
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markers of severe distress, could enable earlier and more accurate prediction of Category 1 emergencies. The integration of such tools into call centre workflows promises to improve decision-making speed
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January 2026 ** (Please see the note below regarding potential later start dates.) Supervisors: Primary Supervisor: Professor Gibin Powathil Secondary Supervisor: Professor Mark Jones Additional Supervisor
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operating filters. Quantify operational performance including headloss recovery, filtrate turbidity, biological stability and lifecycle carbon—using high-resolution sensor data and life-cycle assessment tools
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) of high-value critical assets. Through this PhD research, algorithms and tools will be further improved and developed, validated and tested. It is expected that combining the domain knowledge and the
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for healthcare. A2 Project or subject specific skills; e.g. experience of data collection, the use of AI/ML tools for processing collected data and understanding of hardware technologies to configure systems and
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analyse large, multidimensional 4D STEM datasets. Develop or adapt software tools (e.g. Python, MATLAB) for image reconstruction, phase mapping, and quantitative analysis of ferroelectric domain wall
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autonomy and improved quality of life for women managing menopause. A user-friendly and effective psycho-education tool for menopause monitoring. Publications in peer-reviewed journals and presentations