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- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
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- University of South-Eastern Norway
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minimise such loss through the use of generative AI to augment training sets is proposed. Multimodal sensing: The use of open-loop or simplified (e.g., single-sensor) closed-loop systems in food automation
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, AI risks remain difficult to identify and assess. Many arise not only from technical limitations but from interactions among algorithms, users, workflows, and clinical environments. This requires
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, including user interfaces and algorithms, through participatory approaches that actively involve stakeholders (e.g., technology designers and actors from the public and private sectors). This approach aims
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three-year PhD–positions related to use of AI for mapping of forest ecosystems. New sensors and increased digitalization generate vast quantities of data that together with advanced statistical methods
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are the leading academic IT environment in Norway, and offer a wide range of theoretical and applied IT programmes of study at all levels. Our subject areas include hardware, algorithms, visual computing, AI
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(ENDOTRAIN). Join Europe’s first doctoral network in digital endocrinology – integrating AI, sensor technology, omics, and clinical medicine to transform diagnosis and treatment of adrenal diseases. Digital
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, algorithms, visual computing, AI, databases, software engineering, information systems, learning technology, HCI, CSCW, IT operations and applied data processing. The Department has groups in both Trondheim
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the Faculty of Medicine and Health Sciences at NTNU. It was established as a Centre of Excellence in 2002, 2012, and again in 2023. Our Centre of Excellence, the Center for Algorithms in the Cortex (https
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level and across entire wind parks. Modern turbines are equipped with sensors that collect large amounts of operational and environmental data, yet translating these heterogeneous data streams
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level and across entire wind parks. Modern turbines are equipped with sensors that collect large amounts of operational and environmental data, yet translating these heterogeneous data streams