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Econometrics Virtual power plants Power systems and/or power electronics Machine learning Renewable energy systems Advanced statistics Language requirement: Good oral and written communication skills in English
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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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four years are expected to acquire basic pedagogical competency in the course of their fellowship period within the duty component of 25 %. More about the position / Project description The research
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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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their first years) PhD Fellows may acquire pedagogical competency upon agreement How to apply The application with attachments must be submitted in our electronic recruiting system. Please follow the link
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The idea is to combine established iterative ensemble Kalman methods with novel emerging machine-learning-enabled model calibration techniques recently adopted in CLM-FATES at UiO. The aim is: to constrain
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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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to Norway (international staff may also benefit from tax cuts in their first years) PhD Fellows may acquire pedagogical competency upon agreement How to apply The application with attachments must be
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; machine learning and data science. Experience with one or more of the following computing skills will be considered an advantage: Natural Language Processing and LLMs; R; Python. Applicants must be fluent