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Details Title Postdoctoral Research Fellow in Statistical Machine Learning and Biomedical AI School Harvard T.H. Chan School of Public Health Department/Area Biostatistics Position Description
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single-cell and single-nucleus multi-omics, machine learning/AI, computational biology, and experimental validation to understand cardiovascular disease progression and identify novel therapeutic targets
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and machine learning algorithms to analyse and interpret the acquired data. The successful candidate will work primarily with Dr Qimei Zhang in the Department of Engineering at the Nottingham Trent
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the supervision of the Principal Investigator, including but not limited to the following: Develop new computational tools through the application of AI / deep learning / machine learning / statistics on spatial
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computational chemistry, reaction network analysis, and machine learning for organometallic catalytic reactions. 2. Design of membrane-permeable macrocyclic peptide drugs via machine learning structure
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areas of statistics, data science, and artificial intelligence (AI). The position will focus on developing and applying novel statistical, machine-learning, and AI methods to advance biomedical and
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machine learning or artificial intelligence to biomedical data. Experience managing large research datasets, databases and implementing FAIR research data management principles. Excellent communication and
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, Computational Biology, Computer Science, Biomedical Engineering, or a related quantitative discipline Strong background in machine learning/deep learning (e.g., CNNs, transformers, vision models) Experience
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individuals participating in studies investigating the cardiovascular consequences of preterm birth. By combining fluid dynamics, machine learning, and advanced imaging analysis, the project seeks to develop
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, epidemiology, biostatistics, machine learning or a closely related quantitative discipline. Strong knowledge of analytical methods relevant to epidemiological and biomedical research, including machine-learning