71 machine-learning-"https:"-"https:"-"https:"-"U.S"-"TCAT-Dickson" PhD positions in Norway
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- NTNU - Norwegian University of Science and Technology
- Norwegian University of Life Sciences (NMBU)
- University of Oslo
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- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
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to research, development and demonstration of a methodology for building and integrating machine learning solutions for past technical artefacts. Contributing to the development of holistic view of product
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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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artificial intelligence. In a world where AI systems are reshaping how we learn, work and participate in democracy, AI LEARN tackles the promise and peril of hybrid intelligence—human and machine working and
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, control, AI, machine learning, physics, and related fields, including early-stage researchers eager to contribute to this emerging scientific frontier. Duties of the position Fundamental contributions in
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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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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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is internationally recognized, with interests spanning a broad range of areas - including statistical machine learning, high-dimensional data and big data, computationally intensive inference
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Digital Twin for façade condition, fire safety risk classification, and maintenance planning Apply statistical and machine-learning methods to link climatic loads to degradation indicators Validate models