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you often find yourself fascinated by the multiscale complexity of real-world engineering problems? In this project you will develop advanced multiphase transport models for porous systems, bridging
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? Do you like studying physical systems with wide impact in society? Then, this PhD position is ideal for you! Information This PhD project aims to develop advanced fluid models for low-temperature
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that requires tightly integrated approaches combining control, learning, and uncertainty quantification. This project develops a data-driven control framework grounded in first-principles models, with emphasis
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state-of-the-art tools and AI libraries developed at the TU/e, such as GameBus and Experiencer. The collected data forms the basis for developing predictive AI models that tailor coaching content and
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have an edge between two nodes if the corresponding objects intersect. Such graphs have been used, for instance, to model sensor networks. Geometric intersections graphs are a generalization of planar
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captured from UAVs. The research will address the design of AI models capable of combining heterogeneous sensor modalities, including RGB, thermal, LiDAR, acoustic arrays, GPR, and X-ray backscatter
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) matrix architecture are influenced by the mechanical and geometric properties of their environment. These computational models can provide crucial mechanistic insights into the key parameters governing
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Na+ transport, interfacial phenomena, and performance degradation induced by e.g. salt accumulation or Al deposition. By leveraging your experimental data with the modelling approach
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industrial partners. Experience in model distillation, model adaptation and related topics is a plus. Good academic writing and communication skills. Fluent in spoken and written English (C1 level). Additional
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analytical models for labour market adaptivity Design and develop a scenario-based serious game to support future-making and stakeholder engagement Contribute to high-quality scientific publications and