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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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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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& 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
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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
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and Technology (NTNU) for general criteria for the position. Preferred selection criteria Experience with AI / probabilistic AI / Machine Learning / Reinforcement Learning Experience with numerical
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invites applications for a PhD position focused on developing a theoretical framework for monitoring and updating adaptive learning systems (including machine learning/artificial intelligence systems) under
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selection criteria Experience with AI / probabilistic AI / Machine Learning / Reinforcement Learning Experience with numerical optimization and MPC Strong programming skills (Python, C) Personal