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Machine Learning for engineering. Project description Supervisor: Lucas LESTANDI Keywords: Physics Informed ML, ML for engineering, surrogate modeling, reduced order modelling Topic open: Background
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, particularly numerical fluid mechanics, reduced-order modelling, or machine learning. You are passionate about performing original research at the intersection of these different subjects, and interested in
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to explore Artificial Intelligence (AI)-based techniques for detecting and excluding faulty GNSS measurements [1-2]. AI models, particularly those leveraging machine learning, offer a more adaptive and dynamic
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, Mechanics of Materials, Elasto-Plasticity Topic open: Model-free data-driven approaches offer an interesting alternative to classical constitutive model-based approaches, and to machine-learning approaches
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strong expertise in: • Machine learning, particularly transformer-based models and deep learning frameworks (e.g., PyTorch, TensorFlow). • Multimodal data processing and graph-based modeling