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, numerical mathematics, inverse problems and scientific computing. The successful candidate will demonstrate expertise in the design, analysis, and/or application of mathematical and computational
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of affective computing. The project aims to develop reproducible machine learning approaches for automatic emotion recognition in music, with stronger theoretical grounding, transparent model
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of affective computing. The project aims to develop reproducible machine learning approaches for automatic emotion recognition in music, with stronger theoretical grounding, transparent model
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, and the development of numerical methods for astorphysical fluid dynamics and radiation transport. Projects may employ a range of approaches from analytic modelling and numerical calculations on
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will commence with, in parallel, the survey for collecting the data and a comparison of machine learning methods on artificial pseudo-randomly generated data. The world will see all too many changes
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will commence with, in parallel, the survey for collecting the data and a comparison of machine learning methods on artificial pseudo-randomly generated data. The world will see all too many changes
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ensembles of diverse opponents.” Neural Computing and Applications (2025). https://doi.org/10.1007/s00521-024-10511-9 de Arruda, H. F., Gracia-Lázaro, C., Aleta, A. and Moreno, Y. “Collective
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ensembles of diverse opponents.” Neural Computing and Applications (2025). https://doi.org/10.1007/s00521-024-10511-9 de Arruda, H. F., Gracia-Lázaro, C., Aleta, A. and Moreno, Y. “Collective
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truck. Computing the location and time of these connections remains an unsolved computational challenge. This project will create new algorithms for calculating cost-effective and time-efficient routes
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truck. Computing the location and time of these connections remains an unsolved computational challenge. This project will create new algorithms for calculating cost-effective and time-efficient routes