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Employer
- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
- University of Oslo
- Norwegian University of Life Sciences (NMBU)
- University of Bergen
- Western Norway University of Applied Sciences
- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
- NORCE Norwegian Research Centre
- UNIS
- University of South-Eastern Norway
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Field
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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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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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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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complex biological systems. Research Environment & Collaboration The successful candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable
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, multi-agent systems and data-driven optimization. Basic skills and knowledge of machine learning principles. A good understanding of practical engineering challenges with a view towards impact. Personal
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energy systems and energy markets. Strong interest in distributed optimization, multi-agent systems and data-driven optimization. Basic skills and knowledge of machine learning principles. A good
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& Collaboration The successful candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable implementations. By establishing a new class of multi-frame
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candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable implementations. By establishing a new class of multi-frame factorization methods