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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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understanding of adaptive immune receptor (antibody and T-cell receptor) specificity using high-throughput experimental and computational immunology combined with machine learning. The long-term aim is to
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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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be employed by any other institution for the time of the fellowship. Experience with AI-related research and/or innovation is an advantage. Experience in machine learning is a requirement. Experience
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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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economics of ICTs / AI. Experience with one or more of the following empirical research methods will be considered an advantage: applied microeconometrics and causal inference; machine learning and data
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an advantage: applied microeconometrics and causal inference; machine learning and data science. Experience with one or more of the following computing skills will be considered an advantage: Natural
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an advantage: applied microeconometrics and causal inference; machine learning and data science. Experience with one or more of the following computing skills will be considered an advantage: Natural
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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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using: Snow cover Flux tower data The idea is to combine established iterative ensemble Kalman methods with novel emerging machine-learning-enabled model calibration techniques recently adopted in CLM