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Theory, Department of Environmental and Energy Sciences. Become a part of the team and contributing to research on current and future mobility service usage and attitudes among car and non-car owners
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a doctoral student with a strong background in machine learning, mathematics, and modeling, and an interest in biological systems. The successful candidate will join a project to understand and model
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applications Qualifications Requirements PhD in a relevant field such as Artificial Intelligence, Machine Learning, Computer Science, Computational Science or other field that the employer finds relevant
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. In addition, the following are requirements for the role: Strong programming and quantitative skills, particularly in Python and/or R. Experience in deep learning, machine learning, or large-scale
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assistant will primarily involve preparing and integrating large geospatial datasets, adapting and applying machine-learning models across different forest and environmental conditions, and evaluating model
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-safety analysis. The research will combine empirical crash and traffic data with statistical modelling, machine learning and traffic or driving simulation to study crash occurrence, injury severity and
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machine learning. The project combines methodological research in statistics with applications to large-scale social science data. The successful candidates will have the opportunity to collaborate closely
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thus MLOps (Machine Learning Operations), datacentric AI, and legal and ethical aspects of AI. The empirical research catalyzes industry-academia collaboration and cross-disciplinary initiatives, in
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digital twin collection and analysis of acoustic, mechanical, and anatomical data from toothed whales development and use of computational and analysis code, with or without machine-learning components, in
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. Conduct transportation resilience assessment and enhancement studies based on GIS, complex network analysis, and machine learning. Simulate human mobility in response to extreme weather events (e.g