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application! Your work assignments Spatio-temporal processes are everywhere in science and engineering, with applications ranging from weather prediction to cardiovascular medicine. Developing machine learning
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that learn nonlinear cross-fidelity correlations. More broadly, scientific machine learning methods such as physics-informed neural networks (PINNs) and operator learning (DeepONet, Fourier Neural Operator
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Doctoral Researchers (PhD students) to work on deep learning methodologies for machine and robot perception. These positions are funded by the Horizon Europe project OPERA (Open Perception, Learning, and
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environment. Key research Objectives: AI Innovation (Taxonomic Identification): Developing and optimizing deep-learning architectures (e.g., YOLO) for the automated detection and classification of nocturnal
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Investigator, this role is part of a leading AI research group specialising in reinforcement learning and intelligent systems. The team is focused on producing world-class research while collaborating with
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look forward to receiving your application! Do you have a background in machine learning and interested in telecommunications? You have a chance to contribute to development of sensing methods for new
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performance. This PhD project aims to develop a data-driven framework for graphene aerogel design by integrating structured experimental Design of Experiments (DoE) with machine learning (ML). The student will
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application! Do you have a background in machine learning and interested in telecommunications? You have a chance to contribute to development of sensing methods for new distributed MIMO systems. Your work
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learning from sequential data is available at the Department of Mathematical Sciences at the Norwegian University of Science and Technology (NTNU) in Trondheim. The project will be supervised by Prof
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24th April 2026 Languages English English English The Department of Mathematical Sciences has a vacancy for a PhD Candidate in Mathematical Foundations of Machine Learning for Sequential Data Apply