233 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:" positions in Norway
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research activity. In this project, you will develop fundamental machine learning methods and apply them in an interdisciplinary research environment spanning physics, neuroscience and computational science
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description Integreat - the Norwegian Centre for Knowledge-driven Machine Learning at the University of Oslo invites applications for a doctoral research fellowship. The PhD candidate will work at the
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Machine Learning at the University of Oslo invites applications for a doctoral research fellowship. The PhD candidate will work at the interface of machine learning, statistics, probability, and with
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researchers. The centre is internationally recognized, with interests spanning a broad range of research areas in biostatistics, machine learning and epidemiology and numerous collaborations with leading bio
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inventories and provision of environmental information. Similarly, the developments in AI and machine learning allow for new and improved processing of remotely sensed data supporting precision forestry
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, wearable physiological sensing, and machine learning to uncover how factors like fatigue and cognitive workload impact technician performance. Join us to develop predictive models that predict human error
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spanning a broad range of research areas in biostatistics, machine learning and epidemiology and numerous collaborations with leading bio-medical research groups internationally and in Norway. OCBE is a
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modelling, or modelling of physical/dynamical systems. familiarity with AI/machine learning/system identification techniques and their application to engineering problems. knowledge of digital twin concepts
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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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is internationally recognized, with interests spanning a broad range of areas - including statistical machine learning, high-dimensional data and big data, computationally intensive inference