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. In addition, you must have: a solid foundation in energy technology and a strong understanding of artificial intelligence (AI), machine learning (ML), and data-driven modeling documented experience
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students. Assist in grant proposal applications. Requirements: PhD in Mechanical Engineering, Machine Learning, Artificial Intelligence, Computational Mechanics, Material Science, Industrial Engineering, or
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economic assessments machine learning or proxy-model based methods field scale simulation geological features geomechanics reactive flow The PhD fellow are not expected to master all these topics. Project
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approaches. Machine Learning in Geotechnical Engineering: Utilising data-driven approaches to model and predict soil-structure interactions or other complex geotechnical problems. Reliability-Based
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programming such as Python, R, MATLAB, or other similar programs and experience in using simulation/optimisation models and advanced data handling techniques e.g. machine-learning techniques, statistics
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, fabio.hernandez@uis.no Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/288466/phd-fellowship-in-urban-m… Requirements Research FieldArchitectureEducation LevelMaster Degree or equivalent
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factors. Though not required, we are particularly interested in applicants who use advanced quantitative methods, including computational modeling, machine learning, and/or analyzing structural and
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for such grants. RENEWALL Renewable is subject to performance if the candidate is enrolled in a PhD program - art. 6º, n.4 c) https://diariodarepublica.pt/dr/detalhe/regulamento/950-2019-127238533 https
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technological change driven simultaneously by digitization, the application of artificial intelligence and machine learning to all facets of company, economic, and human data, and a new emphasis on the importance
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Massachusetts. Specifically, this fellowship is focused on machine-assisted visualization. We welcome applications from recent PhD graduates who are interested in these or related fields, particularly those who