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Neuro-Symbolic Methods for Explanation-Based Reasoning with Large Language Models
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Artificial intelligence and machine learning methods for model discovery in the social sciences School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Robin Purshouse
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skills, with the ability to build strong working relationships. Essential Application/interview A high level of computer literacy and experience in the use of Excel and MS Office software. Essential
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demonstrate good knowledge of mathematics, numerical modelling, fluid dynamics and signal processing and be a proficient user of a programming language, e.g. Python or Matlab. Main duties and responsibilities
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degree of accuracy and attention to detail and will have previously supported student or programme related processes. They will also be able to build effective relationships with staff and students. You
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with a high degree of accuracy and attention to detail and will have previously supported student or programme related processes. They will also be able to build effective relationships with staff and
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for classical imagery inspections (e.g. CCTV) in sewer pipes treated with CIPP lining employing powerful semi-analytical and hybrid (numerical+analytical) acoustic simulations. During the project lifetime
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ideal opportunity to join international research programme bridging across disciplines between soil science, analytical chemistry, and geobiology. The post will contribute to the strategic goals
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of parallel computing (GPUs) to speed solution within the optimisation process. Funding Notes 1st or 2:1 degree in Engineering, Materials Science, Physics, Chemistry, Applied Mathematics, or other Relevant
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of numerical methods for solving scattering problems and inverse problems (assessed at: application and interview) Proficiency in scientific programming languages, Julia or Python (assessed at: application and