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contribute to Horizon Europe-funded project ECSTATIC (Engineered Combined Sensing and Telecommunications Architectures for Tectonic and Infrastructure Characterisation - https://ecstatic-project.eu/ ) The role
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/unsupervised learning (regression, classification, clustering), ensemble methods, and deep learning architectures (CNNs, RNNs). Experience with explainable AI (e.g., SHAP, LIME) and radiomics preferred
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Edinburgh Napier University is the number one Scottish Modern University for research in Computer Science & Informatics, Engineering and for Architecture, Built Environment and Planning, as per
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different ML architectures for postprocessing precipitation forecasts over India. Determine how to maximise information extracted from the raw forecasts and how to optimise postprocessing skill for heavy
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learning architectures including generative models, particularly for sequence or structural data (e.g. transformers, graph neural networks, diffusion models) Proved experience in working independently and as
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- the School of Innovation & Technology (SIT) the School of Design, the School of Fine Art and the Mackintosh School of Architecture. The School of Innovation and Technology combines academic study at
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learning architectures including generative models, particularly for sequence or structural data (e.g. transformers, graph neural networks, diffusion models) Proved experience in working independently and as
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related datasets Proficiency in Python for data science and machine learning Possess sufficient breadth or depth of specialist knowledge with deep learning architectures including generative models
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programming skills are UNIX/LINUX, and programming languages such as Python, R or MATLAB. Role Summary Implement and test different ML architectures for postprocessing precipitation forecasts over India
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integration. Provide expert-level technical and research support to the Architectural Engineering Group. Contribute to ongoing research projects, funding applications, and spinout activities, including