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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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develop a strong and transferrable skillset in cell culture/protein expression, NMR spectroscopy, data analysis and food science. You will also have the opportunity to undertake a placement at PepsiCo. Mode
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position is part of a large-scale research project around immune response in neurodegenerative disease. The PhD student will be part of a data science team working on data analysis and experimental design as
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international collaborations with clinicians, regulators, policymakers, and industry partners. You must have a strong background in machine learning, computer vision, and medical image analysis, with publications
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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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packages. Key responsibilities include survey and experimental design, study pre-registration, ethics applications, data collection, data analysis, data management, and dissemination of findings in the form
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circuits and electronics integration. It is desirable that the candidates have sound knowledge in statistical analysis of device output and have experience with failure analysis. The role holder will work
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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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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