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Signal Processing and Image Analysis group (DSB), Section for Machine Learning, at IFI. DSB has seven full-time and five adjunct positions and carries out research across image analysis and machine
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the use of modern machine‑learning methods within applied mathematics—particularly physics‑informed learning, anomaly detection, data‑driven modelling, and the construction of surrogate models grounded in
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generated synthetic representations. The project will also explore machine-learning approaches and efficient imaging strategies, including reconstruction of three-dimensional pore structures from radiography
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representations. The project will also explore machine-learning approaches and efficient imaging strategies, including reconstruction of three-dimensional pore structures from radiography. By linking defect
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for Catalysis and Organic Chemistry at the Department of Chemistry. The group has extensive experience in computational modelling, reaction mechanisms, and machine learning for catalyst design and discovery. Nova
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four years are expected to acquire basic pedagogical competency during their fellowship period within the duty component of 25 %. Project description and work tasks Particle accelerators are engines
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Is the Job related to staff position within a Research Infrastructure? No Offer Description You are keen on contributing to new advances in deep learning methodology for imaging for monitoring marine
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: Preference Learning for LLMs Apply for this job See advertisement About the position Integreat – the Norwegian Centre for Knowledge-driven Machine Learning at the University of Oslo – invites applications
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of the researchers of the DKM group are also affiliated with the Norwegian Centre for Knowledge-driven Machine Learning (Integreat) . The candidate is expected to join Integreat and strengthen the interdisciplinary
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working with machine learning techniques to develop emulators for the theoretical predictions of various observables as function of cosmological parameters. The candidate will develop and use skills in