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machine learning algorithms for the prediction of manufacturing processes in composite materials. Development of user subroutines for finite element constitutive models Validation of model and numerical
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, interpretation, and predictive modelling. We therefore seek a new appointment to add capacity to our expertise in this area. We have particular interest in, but are not restricted to, expanding our data science
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aging. The main task is to develop methods for predicting health outcomes using dynamic and adaptive modeling whilst addressing computational challenges the analysis pose. This will contribute
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an import element in the prediction of reactor-scale operational scenarios providing compatibility to both, required heat and particle exhaust constraints and good fusion plasma core performance. Given
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exploratory analysis on large, multi-dimensional datasets; (b) develop predictive/diagnostic models and algorithms to lead and support clinical/translational research; (c) work with cross-functional teams
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the complex multiscale nonlinear interactions at the origin of such extreme events. In this project, you will develop machine learning-based reduced-order models which can accurately forecast
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processes, targeting annual savings of £280,000. Responsibilities include creating and refining models to predict particle behaviour, calibrating them to 95% accuracy, and establishing sensor systems for real
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the University of Porto (FEUP), under the scientific supervision of Professor Alexandre Ferreira. Grant duration: Initial duration of 3 months, with the predicted starting date in May 2026, on an exclusive basis
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: Initial duration of 6 months, with the predicted starting date in April 2026, on an exclusive basis eventually renewable but never exceeding the project duration. If it is not possible to ensure
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months, with the predicted starting date in april 2026, on an exclusive basis eventually renewable but never exceeding the project duration. If it is not possible to ensure the duration of 6 months