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Field
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, implementation and validation of AI-driven geometry processing methods, developing algorithms and software for automated defeaturing, feature detection and intelligent simplification informed by the governing
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experience with implementing machine learning algorithms. Experience or interest in innovative control systems engineering including IEC 61499 would be an advantage. The candidate will be immersed in a
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of expertise for this position are: (1) Software Engineering (2) Computer Architecture and Operating System (3) Programming and Algorithms. Additionally, knowledge of Artificial Intelligence and Machine Learning
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processes that could be realised in neuromorphic hardware. The research will combine theoretical derivation and simulation-based validation, using mathematical modelling, algorithmic experimentation, and
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learning tools for the prediction of composite manufacturing processes. You will work on development of algorithms, custom written codes, application of commercial finite element software and development
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in reviewing, testing, refining, and providing feedback on historical records that are automatically transcribed, coded, and linked using computer algorithms. Support the project and technical team in
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first publications in the field of explainable AI with a focus on understanding algorithms by various stakeholders, especially at ACM FAccT, ACM AIES, International Journal of Human-Computer Studies or
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of Computer Science is one of the UK's leading Computer Science departments, with an outstanding reputation in research, teaching, and student employability. Our internationally recognised research covers Algorithms
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deep learning algorithms in ESRI ArcGIS or similar software. Desirable Application Proficiency with relevant specialised software and approaches (e.g., geographic information systems, high-performance
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programming tools (e.g. OpenMP, MPI) Accelerator programming (e.g. CUDA, OpenCL, SYCL) Serial and parallel debugging and profiling Parallel numerical algorithms and libraries. System software stack