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Field
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power electronics Machine learning Renewable energy systems Advanced statistics Language requirement: Good oral and written communication skills in English English requirements for applicants from outside
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, and AI chatbot chats; (b) quantitative content analysis; (c)text mining and machine learning methods; (d) survey design and public opinion research; (e) election studies; (f) the Norwegian political
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. Desired: Familiarity with statistical and machine learning techniques. Knowledge about molecular biology and/or gene regulation. Experience with nanopore sequencing, Hi-C, ribosome profiling, or CAGE data
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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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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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, we expect machine learning to be employed to improve accuracy and efficiency of numerical methods, combining advanced technology with scientific research. About the Department of Mathematics at UiB
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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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. The HDL and SPKI research groups are part of the Centre of Research-based Innovation SFI Visual Intelligence that is a center-of excellence in machine learning research. The research groups are also active
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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