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Field
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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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of study at all levels. Our subject areas include hardware, algorithms, visual computing, AI, databases, software engineering, information systems, learning technology, HCI, CSCW, IT operations and applied
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detrimental interactions. To embed grid support functionalities aimed to ensure stability and improve power quality into control algorithms and energy management strategies of the target isolated power system
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Description We are looking for a PhD-candidate interested in topics that lie on the border of optimization by the use of heuristic algorithms and (Explainable) Artificial Intelligence ((X)AI). Specifically, in
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inference algorithms as well as proofs of their correctness and efficiency) and systems (e.g., high performance, functional array programming DSLs) to tackle challenging probabilistic and differentiable
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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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Computing (e.g., memristor modeling/simulation/manufacturing) and Edge AI related areas (e.g., AI algorithms, AI accelerator, VLSI). Background Investigation Statement: Prior to hiring, the final candidate(s
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diverse working day is guaranteed! During the project, you will develop and implement self-learning control algorithms that balance computational demand and modeling precision. You will evaluate, interpret
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are searching for a motivated PhD candidate to design practical over-the-air computing algorithms and protocols for future edge AI applications. About the employer The research of this PhD position will be
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" (Supervisor: Prof Timothy O'Leary) uses principles from systems neuroscience to develop reliable, low-power spiking neural networks and learning algorithms for implementation in a new generation of neuromorphic