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- University of Oslo
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Field
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-working candidate. Main responsibilities Develop and apply machine learning and statistical modeling techniques, including novel AI architectures, for the analysis of complex traits and precision prediction
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, geometric deep learning. Considered an advantage: experience in programming or course work in computer science, algebra, topology or differential geometry, knowledge of topological data analysis or machine
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equations (PDE). Examples of models in the scope of the project include particle models, stochastic PDE and models from fluid dynamics and machine learning. What skills are important in this role
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for forest applications Good presentation skills, written and oral Qualifications that will be emphasized Experience from research in boreal forest ecosystems Programming skills Experience with machine
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optimization. Experience with quality-diversity methods is a plus. • Experience with machine learning and artificial intelligence. • Strong programming skills (e.g., Python, C++), and familiarity with ROS
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pressure-build ups in potential multi-site storage licenses. The research will help to suggest best practices for machine learning integration in de-risking CO2 storage sites. We seek a candidate with a
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Norwegian courses. Required selection criteria You must have completed a doctoral degree in (machine learning, statistics, or similar). You must have a professionally relevant background in algorithms
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Machine Learning. We are looking for a motivated candidate, who has interest in both theoretical, methodological and applied research in anomaly detection in sequential data settings, and who is excited
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in coding e.g. Python, or MATLAB Writing skills for research papers. Experience in applied machine learning, fault diagnosis, laboratory testing and development is a plus. Good knowledge in Norwegian
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to observe next. By combining Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more efficient, intelligent, and impactful. You will integrate field