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to apply it in selected poor-resource settings. This project aims to achieve several objectives, including the development of a new AI-algorithm and a paired dataset for comparing how different imaging
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the areas of Data Science and Informatics as defined by the Bayes Centre and Huawei. Strong background and expertise in LLMs, edge systems, deep learning algorithms and computer systems. Experience and
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working experience in relevant fields. Demonstrated ability to program in C/C++, Python and/or MATLAB to a high standard, experience with state estimation and factor-graph algorithms and working experience
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substantial working experience in relevant fields. Demonstrated ability to program in C/C++, Python and/or MATLAB to a high standard, experience with state estimation and factor-graph algorithms and working
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publications is a plus. Experience in designing, developing, and implementing computer vision models and algorithms. Proficiency in Python and its standard coding practices and common libraries. Experience with
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: Design and implement models for a knowledge graph as part of an R&D team. Research methods and techniques for populating the knowledge graph. Develop models or algorithms to facilitate risk identification
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of this Research Officer is to work on Computational Solids Mechanics particularly developing computational coupled algorithms for thin and slender structures (e.g., beams, rods, shells) made of soft magneto-active
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) – semester abroad for an Integrated Master’s programme in Computational Science. The module is structured in two parts Linear Programming, covering model formulation, the graphical solution method, the simplex
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community. Key Requirements Bachelor’s degree in Computer Science, Mathematics or related discipline or equivalent working experience. MSc or higher degree in Computer Science, AI, Data Science, or equivalent
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require metrics that predict how well a given hearing aid algorithm will perform for a specific user in a particular acoustic environment. Existing approaches often rely on oversimplified assumptions about