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develop (physics-informed) hierarchical graph neural network architectures that can capture the complexity of multi-scale urban energy infrastructures. The PhD will explore how these models can represent
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frequent cloud contamination. This scale mismatch prevents a coherent representation of radiative–thermal processes at the urban scale. This PhD will develop physics-informed deep learning models for data
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equitable urban energy systems. Our work combines technology and policy with systems thinking and practical implementation, always grounded in real-world urban challenges. This PhD position is offered in
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rural-urban region in the Netherlands, using a research through design approach; Setting up and carrying out the above-mentioned research, resulting in, among other things, a PhD dissertation containing
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8 Oct 2025 Job Information Organisation/Company École Polytechnique Fédérale de Lausanne (EPFL) Department School of Architecture, Civil and Environmental Engineering (ENAC) Research Field
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Offer Description The work will be performed in the team Cascales (https://www.cascaleslab.fr ) of the Laboratoire d'Ingénierie des Systèmes Macromoléculaires (LISM). It is a joint PhD fellowship between
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architectures. PhD students can work on any of the topics spanned by the group, or on closely related areas that meaningfully connect to our research. We expect candidates to explain their research interests and
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intermediate research results in the Netherlands and at international scientific conferences; Actively participating and contributing to the PhD community of Fertile Soils and the Landscape Architecture and
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One fully funded, full-time PhD position to work with Alessandro Suglia in the Embodied, Situated, and Grounded Intelligence (ESGI) group at the School of Informatics, University of Edinburgh
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The PhD student will join a world-leading research environment at the Chalmers Security and Privacy Lab . Software is often the root cause of vulnerabilities in modern computing systems. We develop