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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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: PhD in Materials Science, Chemistry, Physics, Computer Science, or a related field. Strong expertise in machine learning for materials science (e.g., generative models, neural networks, active learning
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or Machine Learning). The master thesis must be included in the application. Documented proficiency in English, please see requirements below. Documentation of skills in English language Strong communication
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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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to apply advanced AI models in areas such as catalyst design, multi-scale modeling, and spectroscopic analysis. The Research Fellow will take on a significant role in machine learning theoretical energy
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. Perform any other duties relevant to the research programme. Job Requirements: PhD in Computer Engineering, Computer Science, Electronics Engineering or equivalent. Independent, highly analytical, proactive
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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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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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at the Institute of Genetics and Cancer. Informal enquiries may be directed to Dr Athina Spiliopoulou (A.Spiliopoulou@ed.ac.uk ). Your skills and attributes for success: PhD in machine learning, genetic epidemiology
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by using explainable AI To develop generative AI techniques to design novel biologics for cancer Job Requirements: Preferably PhD in Computer Engineering, Computer Science, Electronics Engineering or