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, reliability, and availability of complex industrial systems while making maintenance strategies more cost-efficient. Together, UESL and IMOS are seeking a motivated and qualified PhD candidate to advance
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plus. You enjoy working with complex, multimodal datasets and developing robust algorithms for continuous monitoring and predictive modelling. You are comfortable combining coding, data analysis, and
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Networks for Multi-Scale Urban Energy Systems Your tasks The focus of this research is to design and develop (physics-informed) hierarchical graph neural network architectures that can capture the complexity
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crucial insights. In this project, you will contribute to the development of AI-driven methodologies for experimental fluid mechanics , focusing on: Designing multi-fidelity neural networks for adaptive
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. Key qualifications include: Proven ability to develop and apply complex in vitro or ex vivo test systems. Hands-on experience with microbial assays, biofilm models, and/or biomaterial characterization
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and scaled around innovative climate adaptation or mitigation solutions Deep expertise in relevant quantitative and/or qualitative research methods, e.g. expert interviews, social network analysis