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Field
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time role, 0.1FTE. The activities of this role will support development of future proposals for funding, into AI for renewable energy. You will consider ways in which the integration of machine learning
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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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experiments, perform data analysis, and create computational models of learning and memory. A PhD is required. An ideal candidate will be: highly motivated with a record of high scientific productivity, possess
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, Bayesian and maximum likelihood approaches, spatial statistics and random forests or other machine-learning approaches and be quick to learn new techniques. Enjoyment of analysis of large and spatially
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-atomic potentials using a combination of classical and machine-learning (ML) approaches (and a new hybrid method recently developed in our group). Some of the types of simulations that will be performed
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simulations using DFT (particularly of surface processes); kinetic Monte Carlo simulations; molecular dynamics simulations; classical and machine-learned force fields. Highly developed skills in scientific
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, Information Science, Software Engineering, Data Science and Business Analytics. At the postgraduate level, we offer research master’s and PhD opportunities – including specialist taught masters in Artificial
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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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candidate in the area of machine learning for IoT networks. The candidate must hold (or about to complete) a PhD in the related fields shown below. The candidate is expected to have hands-on experience in
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Associate/Research Fellow with expertise in Artificial Intelligence (AI)/Machine Learning (ML), pedagogical research in Institutes of Higher Learning (IHLs), and web/mobile application development (both