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UNIX/Linux interface and basic programming (e.g. Python) is a requirement. Experience with machine learning is an advantage. Experience from free energy calculations is an advantage. Applicants must be
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computing language. Experience with machine learning methods is a plus. The research fellow must take part in the faculty’s approved PhD program and is expected to complete the project within the set
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engineering, engineering computing, sensor networks and measurement technologies, grid computing and physics data analysis, machine learning, and interactive and collaborative systems. The prospective PhD
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and master's level in engineering and science, as well as PhD education in computer technology. The Mohns Center for Innovation and Regional Development researches innovation and offers master's
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-scale assessment data, meta-analyses of meta-analyses) Methods and approaches to cumulative, living, and community-augmented meta-analyses Methods and approaches to include machine learning and artificial
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engineering, engineering computing, sensor networks and measurement technologies, grid computing and physics data analysis, machine learning, and interactive and collaborative systems. The prospective PhD
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-compliance in ecological momentary assessment, or exploring the use of machine learning techniques to aid the estimation of item response theory (IRT) models in small samples. The ideal candidate has prior
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, sensor networks and measurement technology, grid computing and physics data analysis, machine learning, and interactive and collaborative systems. The prospective PhD candidate will be part of a research
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laboratory in the department. The PhD position will be supervised by Professor Natasa Nord at NTNU. Are you ready to take your research career to the next level? We offer an exciting three-year Postdoctoral
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project/work tasks: The SnowAI project aims to use to produce new high-resolution datasets on snow depth in Western Norway derived from machine learning and radar remote sensing. The successful PhD