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Field
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power engineering. In condition monitoring non-invasive data is analyzed through machine learning algorithms or by statistical methods. The aim of predictive analysis is to use non-invasive methods
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single synthetic program of computational geometry. Specific interests include morphology, design topology, discrete differential geometry, packings, and machine learning methods for unstructured geometric
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computational modeling, geometric morphometrics, multivariate and Bayesian statistics, spatiotemporal and spatial modeling (including GIS), causal inference, machine learning, AI, and statistical software
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transportation operations and network modelling, accessibility analysis, data analysis (statistics and/or machine learning methods), and spatial mapping. Because the work will involve multiple years of daily
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tunnel experiments is a plus • Experience in programming and AI/machine learning is a plus More Information Location: Kent Ridge Campus Organization: College of Design and Engineering Department
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. Desirable: Proficiency in scientific programming (e.g. Python) and familiarity with data science and machine learning techniques. Experience with geochemical analytical techniques and working in a laboratory
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to quickly learn new skills Ability to work independently and without significant supervision Excellent verbal and written communication skills Intermediate proficiency in Google Suite and Teams/MS Office
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for disease transmission. Research activities will include: Collaborate with subject matter experts to learn about the development of analytical tools for assessing Highly Pathogenic Avian Influenza (HPAI
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, policy, energy conversion, new business models, techno-economic and life cycle analyses, machine learning, optimization, AI, intelligent networks, among others. The PDF will join a project studying
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in several areas. These include, but are not limited to, Using MPS to identify new potential targets for future medical countermeasure development Learning development and characterization