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development of computer systems for data analysis, development of machine learning methods, and the clinical use of technology. Within the research groups you will therefore work together with computer
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volumes, there is a plan to utilize modern machine learning strategies like "physics-informed neural networks." One of the main advantages of this approach is that measurements and observations made
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Develop and apply machine learning techniques and statistical analyses, including digital twin methodology, to fit and validate prediction model. Perform quality control and imputation of genotype and
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Engineering or any other related field. You must meet the requirements to access and perform research on dual-use technologies according to Norwegian regulations on Control of the Export of Strategic Goods
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will be adapted to the candidate’s background and the evolving needs of the center. Possible directions include the application of rock physics models, Bayesian inversion methods, and machine learning
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relevant experience in the development and deployment of machine/deep learning models as well as the use of remote sensing data You must have relevant experience in the development of hydrodynamic and water
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physics models, Bayesian inversion methods, and machine learning algorithms in the electromagnetic context. Qualifications and personal qualities: Applicants must hold a master’s degree (or equivalent) in
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studies. Proficiency in relevant computational tools and statistical methods. Experience with machine learning in large datasets. Interest and motivation to work in a multidisciplinary team. Ability to work
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, The Norwegian Centre for Knowledge-driven Machine Learning (ML), a center of excellence funded by the Research Council of Norway. The center is in operation from 2023 to 2033 and will fund more than 60 PhD and
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data collection approaches. Familiarity with or strong motivation to learn machine learning or advanced data analytics for pattern detection and forecasting in environmental data. Familiarity with