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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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more cost-efficient. Together, UESL and IMOS are seeking a motivated and qualified PhD candidate to advance the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By
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, geography, and artificial intelligence to develop coherent and resilient approaches for urban energy infrastructures under land-use constraints such as No Net Land Take. The consortium comprises four
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Ph.D. Position in Organic Chemistry, Polymer Chemistry, and/or Sol–Gel Chemistry & Materials Science
scientific objectives include: Elucidating how monomer structure, solvent choice, and oligomer formation govern polymerization pathways, phase behavior, and network evolution Understanding polymer–solvent
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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