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that the solutions provided are correct. It will involve a mix of algorithm engineering and formal methods, alongside more traditional software engineering activities. This project involves developing software which
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UKRI under Grant Reference EP/X026167/1. In this role, the successful candidate will collaborate with QSL’s team of researchers to design and implement variational quantum algorithms, resource estimation
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econometric analysis and preparing results tables, managing large data sets, handling spatial data, applying machine learning algorithms, conducting computationally intensive statistical analyses, summarizing
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the total compensation value with benefits. Qualifications Experience developing software to take practical advantage of state-of-the-art ML algorithms and research results in AI. (Required) Experience
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literature. Foundational Algorithms for AGI AGI will not emerge from scaling existing models alone; it requires a new algorithmic foundation for learning, reasoning, and adaptation. This research area is
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, and use smart phone apps to collect passive and active data using a prospective observational cohort study design. We will use this data to develop and validate a personalised risk prediction algorithm
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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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and outliers in claims and develop trends and patterns for potential cases. Develop algorithms, queries, and reports to detect potential FWA activity. Analyze member records and claims data to ensure
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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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for the position of assistant professor in the project NCN MAESTRO "Challenging problems in partial differential equations inspired by cutting-edge algorithms in statistics and machine learning