358 parallel-computing-numerical-methods positions at University of Sheffield in United Kingdom
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to plasticity. (assessed at: application/interview) Experience in computational mechanics, especially numerical methods for solving field equations relevant to material mechanics, i.e., Finite Element schemes
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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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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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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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or machine learning methods to tackle predictive questions. Proficiency in building and validating statistical methods and/or machine learning techniques in R or Python are also essential. Applicants
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with career stage). Essential Interview / Application / Test In-depth knowledge of Computational Intelligence/Machine Learning systems and methods, in particular those relevant to Explainable AI, Physics
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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