667 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" positions at University of Sheffield
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reputation for being the world-class solution provider in the field of AM. Develop a clear strategic plan of engagement/representation at relevant conferences and disseminate information about new technologies
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information exchange. In this project, you will explore different facets of the following problems • Geometric interpretation of a distributed nonlinear controller in terms of controllability and reachability
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. Requirements: 1st or 2:1 degree in Engineering, Physics, Mathematics, or other Relevant Discipline. For further information please contact Dr Jose Curiel-Sosa (j.curiel-sosa@sheffield.ac.uk) Funding Notes
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Traditional machine learning (ML) approaches require large volumes of annotated defect data and significant manual oversight. For the aerospace industry, this, combined with rare, but critical, defect examples
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Molecular Biology experimental techniques, such as PCR and molecular cloning and experience with analysing data from high-throughput sequencing experiments, including analytic pipelines, and the development
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& Castello, 2024). Applicants will also likely draw upon seminal frameworks to help make sense of the data, such as that of legitimate peripheral participation (Lave & Wenger, 1991), and upon writing on AcLits
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Targets in Amyotrophic Lateral Sclerosis Using Patient-Derived Models and Single-Cell Multiomic Data" Host Institution: University of Sheffield Primary Supervisor: Dr Richard Mead Secondary Supervisors
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of effective & efficient healthcare interventions. You will design and deliver cost-effectiveness and budget impact analyses, analyse healthcare cost and outcomes data, and develop decision-analytic models
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protocols, and analysing data, and who is motivated by translational work that is intended to function in real patients rather than remain as a laboratory demonstration. Entry requirements Candidates must
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field data, the research assistant will help assess the accuracy, robustness and operational value of these algorithms for large-scale forest inventories and the detection of endangered species