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Field
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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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relevant field (Informatics, Computer Science, Ecology, or Biology). strong Python programming skills proven experience with Machine Learning and image processing (Deep Learning/Object Detection) strong
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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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representations of time‑dependent data through sequences of iterated integrals and have recently gained significant attention in machine learning and data science. The project will investigate how
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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
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programming models and high-performance computing techniques and machine learning models. Practical experience in the programming of high-performance computing of AI and/or scientific computing applications