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programme aims to advance fundamental understanding of heat transfer and turbulence physics in wall-bounded flows through numerical simulations, data-driven modelling, and machine learning techniques. Key
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the following ones. Exploration of active auditing techniques for large machine learning models, use of reinforcement learning, potential application to recommender systems. The PhD will mainly investigate
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, machine learning (PINN, supervised learning) - Python/PyTorch programming - Autonomy, curiosity, and adaptability - Excellent writing skills Specific Requirements The doctoral student's host laboratory is
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intelligence called hybrid AI, integrating data driven learning techniques and symbolic or mathematical models that permit us to express constraints and to carry out logical reasoning. ANITI also has ambitious
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] Cross, E. J., Gibson, S. J., Jones, M. R., Pitchforth, D. J., Zhang, S., & Rogers, T. J. (2021). Physics-informed machine learning for structural health monitoring. Structural health monitoring based
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models, multi-view computer vision, semantic graph-based representations, and self-supervised learning—to automatically interpret and understand complex surgical procedures. The overarching goal is to
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/ computer vision and pattern recognition, including but not limited to biomedical applications Strong interest in applied machine learning, including but not limited to deep learning Experience utilising GPU
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will build on recent advances in machine learning for dynamical systems to extract meaningful representations of complex flame dynamics, construct prognostic ROMs, and perform data assimilation
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knowledge of multi-objective problems. Master students or Engineers in the field of Process Systems Engineering are strongly encouraged to apply. Knowledge of machine learning algorithms, energy markets and
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that are transforming many sectors today through language models, recommendation systems and advanced technologies. However, modern machine learning models, such as neural networks and ensemble models, remain largely