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modes, effects, and criticality requires deep domain knowledge and careful analysis. Collecting High-Quality Sensor Data. Simulating Realistic Fault Conditions. Developing Reliable Fault Prediction Models
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atmospheric perturbations, and improving performance under realistic operational conditions. Main activities include: • Designing and developing deep learning models to correct wavefront sensor nonlinearities
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, SEM, XRD, or particle size analysis Understanding of hydrometallurgical or battery recycling processes Integration of modeling or theoretical predictions with experimental work Collaborating in
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solutions for vibration and noise control in lightweight structures (https://cordis.europa.eu/project/id/101227712 ). The project focuses on the development of Acoustic Black Hole (ABH) technologies
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intelligent decision architectures, predictive analytics, and adaptive computational models that can operate in dynamic, uncertain, and high-stakes project environments. The appointee will conduct original
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, combined with a predictive operational insights model to gain superior operational performance. Employed and supported by an academic team from the University, you will be based at ELE Advanced Technologies
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, and/or computational modeling. This position integrates rigorous experimental characterization with multiscale simulation to understand failure mechanisms and improve safety at the cell, module, and
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ductile deformation by a dislocation creep flow law. Recent data on low-temperature dislocation dynamics predict a smaller peak resistance at the brittle-ductile transition, which favor deformation
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predictive models, and interpreting large environmental datasets, collaborating in interdisciplinary projects and in the production of scientific publications. In the performance of duties, it may sometimes be
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. Combining AI-based prediction (e.g., TCNN, LSTM, etc) with musculoskeletal models to estimate and predict muscle activation and tendon force over short horizons (e.g. ~200 ms). Integrating these predictions