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Show all breadcrumbs Postgraduate Research in Computational Imaging and Machine Learning in the Department of Radiology and Biomedical Imaging Monday, June 1, 2026 Title of the Position
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availability of funds. We seek a scholar whose research advances spatial statistics, statistical learning, machine learning, or artificial intelligence (AI) for understanding geographic processes and social
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advanced machine-learning and AI methods for complex engineering and industrial systems, with a particular focus on improving their reliability, availability, and operational performance while
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approaches for drug design by combining state-of-the-art machine learning with physicochemical knowledge and molecular modeling. Representative publications from our group include: https://doi.org/10.1038
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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
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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
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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
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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
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-supervised machine learning methods for biological data where reliable labels are scarce, expensive, or impossible to obtain. The aim is to train models on synthetic data generated by biophysical
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Mapping Site (UMS), a unique test environment for utility mapping technologies. Current machine learning models and their training data are limited in size, comprehensiveness, and realism – resulting in