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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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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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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
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Algorithms Agile Programming Applied Machine Learning Computer Graphics Web Design and Programming Cloud Technologies Advanced Databases and Big Data Introduction to AI Natural Language Processing DevOps Data
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, applying and validating your algorithms through laboratory experiments. A PhD in Chemistry, Biology, Computer Science, or related fields with a focus on protein engineering. Proficiency in Python programming
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programming, algorithm development, and mathematical modelling. C2 Research creativity and cross-discipline collaborative ability as appropriate. C3 Excellent communication skills (oral and written), including
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with MoniRail Ltd and Durham University. The primary focus will be on designing and implementing deep learning and anomaly detection algorithms to analyse large-scale, real-world sensor data collected
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computational algorithms of different datasets, and implement novel algorithms within the framework of existing code, providing documentation and user support. You will be responsible for leading statistical
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, architecture-based attacks (e.g., Spectre) show the urgency of addressing these important problems today. Even when low-level programs are well synchronised, the design of the underlying concurrent algorithms
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developing new methods and techniques that will improve standard ML algorithms so as to achieve good performance outside their training distribution, by treating high-dimensional problems as an explicit