25 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" research jobs at University of Oslo in Norway
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(National University of Singapore). The goal of RESPIRE is to develop Machine Learning (ML) solutions for sleep-related disorders of infants and patients with chronic obstructive pulmonary disease
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/english/research/groups/dsb/index.html) as part of Visual Intelligence (http://visual-intelligence.no) , Norway's leading research centre in deep learning for image analysis. Starting date as soon as
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of Anomalies ” (SODA), newly funded by the Norwegian Research Council and affiliated with Integreat – the Norwegian Centre for Knowledge-driven Machine Learning. We are looking for a motivated candidate, who
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particle models, stochastic PDE and models from fluid dynamics and machine learning. What skills are important in this role? Qualification requirements: The Faculty of Mathematics and Natural Sciences has a
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of partial differential 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
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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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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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-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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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