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Field
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algorithms and efficient programming. You can show a proven track record in object-oriented programming as necessary in larger software development teams. You are capable of scientific programming in Python
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. The task of the theory group led by Prof Kyriienko at the University of Sheffield within the consortium is to lead nationally the development of quantum machine learning (QML) algorithms. The research will
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scientific publications, patents, and seeing collaborators translate our work into real-world settings. You will be responsible for developing machine learning and AI algorithms for a range of data and
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into real-world settings. You will be responsible for developing machine learning and AI algorithms for a range of data and applications (e.g. natural language processing, multivariate time-series data
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resolution. The development of new imaging modes will also require new acquisition and computational processing techniques, and you will work closely with the theory and algorithmics project group. Ultimately
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criteria Familiarity with the regulatory environment around Deep Learning or Machine Learning algorithms Experience applying quality system standards, software development standards and regulation, e.g. ISO
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, post-docs and interns collaborating across universities to build better algorithms, software tools and benchmarks to assess the safety of AI implementations at the software and hardware level. We
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mix of algorithm engineering and formal methods, alongside more traditional software engineering activities. This project involves developing software which is both mathematically rigorous, and
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variants of importance sampling. We will connect these methods to modern formulations of Monte Carlo algorithms to improve their accuracy, scalability, and overall computational cost. The methodology so
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or more of: the use of micro/nanofabrication and materials characterization tools; computational multi-physics/electromagnetics modelling and/or the application of machine learning algorithms; experimental