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prediction, a machine-learning surrogate model based on Gaussian process regression will be developed and trained using datasets generated by the high-fidelity numerical solver. The surrogate will emulate key
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and kinematic models with machine-learning-based channel state information (CSI) prediction to enable robust, low-latency connectivity across multi-layer NTN systems. This PhD project sits
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equations to simulate pollutant transport, mixing and biochemical processes. To enable rapid prediction, a machine-learning surrogate model based on Gaussian process regression will be developed and trained
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(CHF), tailored to complex geometries typical of fusion reactor cooling systems. Compile a comprehensive dataset of boiling parameters to support machine learning-based analysis of two-phase flow
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children. Mechanistic modelling of disease transmission involves the use of computer code to represent the epidemic dynamics of infectious disease spread within the community. This allows modellers
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education to enable regions to expand quickly and sustainably. In fact, the future is made here. Are you interested in learning more? Read about Umeå university as a workplace Application deadline 2026-02-27
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. Mathematical skills: Competence in mathematical modeling of dynamic systems and probabilistic frameworks. Experience with machine learning or AI methods for localization or perception (e.g. learning-based SLAM
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energy applications. You must have experience with data analysis, machine learning, or AI-supported methods applied to engineering or safety problems. PLEASE NOTE: For detailed information about what the
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education to enable regions to expand quickly and sustainably. In fact, the future is made here. Are you interested in learning more? Read about Umeå university as a workplace Application deadline 2026-02-27
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Liefvoort. For questions about this particular research project, please contact Cécile de Morrée. Would you like to learn more about what it’s like to pursue a PhD at Radboud University? Visit the page about