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PhD Research Fellowships: Artificial Intelligence Adoption, Sustainable Finance, and Twin Transition
knowledge of artificial intelligence and knowledge of natural language processing. Proficiency in statistical analysis, such as econometrics and machine learning for survey data analysis. Experience with data
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to complete the final exam. Desired: Familiarity with statistical and machine learning techniques. Knowledge about molecular biology and/or gene regulation. Experience with nanopore sequencing, Hi-C, ribosome
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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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or more of the following empirical research methods will be considered an advantage: applied microeconometrics and causal inference; machine learning and data science. Experience with one or more of the
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important for renewable energy production and production variability will be an advantage. Knowledge of machine learning or optimization will be an advantage. Applicants must be able to work independently and
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, Machine Learning, Complex Systems Modelling, Space Physics, and Ultrasound, Microwaves, and Optics. The department provides education at the Bachelor, Master, and PhD levels. Contact For further information
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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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/mathematical-cognition-and-literacy . The group collaborates with national and international experts in mathematical cognition, mathematics education, linguistics, and machine learning. Your immediate leader
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degree (M.Sc.-level) corresponding to a minimum of four years in the Norwegian educational system is required. The candidate must have interest and solid background in software systems, machine learning
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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