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must be in a position to register by October 2025 Description:This Industrial PhD studentship has been developed between Salford University and APOS. Gait rehabilitation is emerging as a promising
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techniques from optimization and control theory, scientific machine learning, and partial differential equations to create a new approach for data-driven analysis of fluid flows. The successful applicant will
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the experiment design, impact analysis and icing code droplet impact solver refinement. The main impact of the work will be to provide droplet impact data and analysis at high speeds which are closer to real-time
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about computer data analysis and willing to learn. Laboratory training will be provided but a steady hand is needed for accurate small volume pipetting. You will be working in a team and expected to
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for a pre-doctoral academic with an interest in primary care cancer diagnosis and will lead to skills development particularly in analysis of observational data and may lead to opportunities to employ
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. interviews, participatory creative workshops, data analysis, writing of academic publications). They will also support event coordination and the maintenance of project records and bibliographic databases
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of good-quality data is typically limited for high-value critical assets. This PhD project will focus on developing, evaluating, and demonstrating physics-informed machine learning or domain knowledge
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experiments; supporting other group members with data analysis and interpretation from both simulations and experimental data; and use the developed framework to design new materials with optimised performance
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, stress markers). Ability to conduct quantitative and qualitative research, with experience in data collection, analysis, and interpretation. Willingness to work across disciplines, integrating health
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schedule of questions for research interviews. Conducting a series of semi-structured interviews at UoN. Transcription and analysis of qualitative data. Attending research team meetings. Contributing