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Field
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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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for more than 12 months in the 36 months immediately prior to your recruitment. Skills: Strong interest in AI/Machine Learning, Bayesian modeling and decision-making. Benefits Competitive Salary: Living
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07.04.2026, Academic staff PhD position at the interface of computational physics, machine learning, and experimental reactor design. The project focuses on developing PINN-based simulations
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methods, complemented by simulations of beta-decay chains relevant to post-fission energy release. Neural networks and other machine learning techniques will accelerate the discovery of radiation-resistant
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, methods, models, and algorithms that integrate general and domain-specific knowledge with data, laying the foundations of next generation machine learning. We do this by combining the mathematical and
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identification, optimization, or numerical methods is valuable, as is knowledge of data analysis and machine learning for complex, high-dimensional systems. Programming experience in MATLAB or Python, and an
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graphics. Classical representations are relatively easy to render, while being difficult for generative machine learning models. A recent breakthrough in this area is the Neural Radiance Field (NeRF) and
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Computational Mechanics. Solid background in continuum mechanics and numerical modeling Strong interest in machine learning and scientific computing Experience with numerical methods for PDEs and data-driven
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learning approach to translating multi-modal inspection data into remaining useful life predictions; and (3) create a dynamic techno-economic model linking real-time condition assessments to optimal
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regimes; and machine learning, capturing complex nonlinear behaviour at the cost of model opacity. BENEFIT synthesises these paradigms by integrating stability analysis directly into machine learning