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Field
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data and machine learning processing pipelines. It will also focus on developing AI algorithms for semantically enriching data assets with domain knowledge, which feed into building knowledge graphs
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to the development of innovative and sustainable low carbon plastic waste management and recycling solutions. In this project the post holder will develop novel algorithms and methods for analysis of plastic data
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both the algorithms for robot co-design, and the real-world evaluation of the designs that emerge, as well as providing your own research contributions. Your specific role will vary depending on project
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Learning, Algorithms, Noise Handling (Error Correction/Mitigation), and Verification. These roles are part of the Quantum Software Lab (QSL, link: https://www.quantumsoftwarelab.com ), in collaboration with
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grant, have worked to identify the sampling algorithm used by the brain, to show how the identified sampling algorithm can systematically generate classic probabilistic reasoning errors in individuals
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range of computing subjects, including Cyber Security, Programming, Algorithms, Computer Logic and Architecture, Software Engineering, Database Design, both at undergraduate and postgraduate level
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institutions, and leading industry partners. The successful candidate will contribute to the delivery of high-impact research projects involving AI algorithm evaluation and image data analysis. You will play a
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with the regulatory environment around Deep Learning or Machine Learning algorithms Experience applying quality system standards, software development standards and regulation, e.g. ISO 13485, IEC 62304
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for application to fusion reactors, considering factors such as muon flux, detector sensitivity, and data analysis algorithms. PhD candidates will be tasked with developing simulations of complete detectors and
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leading role in advancing theoretical and algorithmic research in the domain of probabilistic preference aggregation, contribute to the design and analysis of normative frameworks and aggregation rules, and