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Postdoctoral Researcher in Machine Learning of Isomerization in Porous Molecular Framework Materials
broad range of applications. Computational chemistry and Machine Learning increasingly underlies MFM research to search or screen candidate MFMs prior to synthesis. A major drawback when applying
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, machine learning models will be used to produce planning models for potential e-scooter/e-rickshaw stations (docking and charging). Findings from this project will contribute to evidence-based, community
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Overview This PhD project will take a comprehensive approach to exploring how corporate sustainability disclosure practices evolve in response to the integration of Machine Learning (ML) within
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candidate will develop machine learning and algorithmic design skills. The candidate will gain valuable multidisciplinary skills in the area of machine learning and data analytics methods and their
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the development of climate change models of fashion supply chains and the design of computer model laboratory experiments (with students) that will aim to determine what and how participants learn from these models
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prediction model; specifically, those models that utilize network analysis and machine learning techniques. The aim of this project is to attempt to evaluate the relative strengths of this new strand