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transfer, fluid–solid interactions, and pressure drop in complex thermal structures. Design and train physics-guided surrogate models (e.g. neural networks with embedded physical constraints) for rapid
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. The research will focus on identifying and characterizing ultrasonic signatures emitted by aging electronic components, and on developing physics-informed neural networks (PINNs) to model their degradation
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do they also influence the early recognition of these symptoms by family members and the healthcare team? What links might these factors have with clinical, cognitive, or neural data? Finally, how do
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neural networks. • Establishment of the evaluation setup, including collection and preparation of sports datasets (results, game statistics, athlete data) and definition of benchmarks and metrics
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Sklodowska-Curie Doctoral Network linking 21 academic, cultural, and industrial partners to develop advanced nondestructive evaluation and data-driven digital tools for paintings and 3D artworks (https
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. 2) the development of computational models to enquire about the mechanisms that enable heterogeneous representations in neural networks. These models will be informed by experimental data. Duties
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combining classical training with quantum-enhanced sampling. Hamiltonian & Lindbladian Learning: Designing scalable neural networks to reconstruct the dynamics of open quantum systems (e.g., neutral atom
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architectures to solve data-driven sensing and control problems related to turbulent atmospheric flows. The work will center around investigation of reinforcement learning and convolutional neural networks
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artificial intelligence and neural networks, with a particular focus on applying these numerical approaches to quantum many-body systems, such as correlated 2D materials, quantum Moire systems, frustrated
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. Additional Qualifications: Knowledge of machine learning tools such as penalized logistic regression, XGBoost, neural networks. Knowledge of modeling strategies including propensity score weighting and doubly