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experience in spatial analysis and/or machine learning methods, and an interest in applying these tools to urban and housing policy questions. The Fellow should demonstrate potential for producing high-quality
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of publications. Criteria Essential or desirable Stage(s) assessed at A PhD (or close to completion of a PhD) in Machine Learning or a similar area (e.g. in Computer Science, Electrical and Electronic Engineering
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spatial transcriptomics and imaging genomics projects, integrating bulk and single-cell RNA-seq datasets, and applying advanced statistical and machine-learning methods (AI/ML) to extract novel biological
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spatial transcriptomics and imaging genomics projects, integrating bulk and single-cell RNA-seq datasets, and applying advanced statistical and machine-learning methods (AI/ML) to extract novel biological
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/2025 Role Description An exciting opportunity for an established researcher or a recently completed PhD researcher with experience in malacology, epidemiology, data mapping and/or schistosomiasis
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annotation of these metabolomes using multistage fragmentation (MSⁿ) data, incorporating novel computational methods and strategies (e.g. spectral matching, network-based approaches, machine learning) where
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challenges from low carbon shipping and sustainable fuels to solar power technologies and advanced brain models. Learn more at https://mecheng.ucl.ac.uk . Within this dynamic environment, the Moazen Lab is
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language requirement of the UK HEI; Have a background or a proven interest in AI foundations and its application in civil and environmental engineering, including machine learning, sustainable construction, climate
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language requirement of the UK HEI; Have a background or a proven interest in AI foundations and its application in civil and environmental engineering, including machine learning, sustainable construction, climate
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to £41,478 per annum. An exciting opportunity for an established researcher or a recently completed PhD researcher with experience in malacology, epidemiology, data mapping and/or schistosomiasis modelling and