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PhD Student Position in Interoperable Digital Workflows for Regenerative Building Design and Perform
), Life-Cycle Assessment (LCA), embodied and operational carbon, circularity and material-reuse indicators, building monitoring, sensor data, Post-Occupancy Evaluation (PoE), and feedback from building
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Mission The Data-Driven Mechanics Laboratory invites applications for a fully funded PhD position at the intersection of computational mechanics, multiphysics of geomaterials, and interpretable
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Observation, environmental modelling and geospatial data science. The position is part of Vertical Africa (VERTICAF), an interdisciplinary research project funded by the Swiss National Science Foundation
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Your mission We are offering a challenging position as a full-time (100%) PhD Student at ENAC Faculty at EPFL, Sion. As a PhD student, you will develop technology for humanitarian data mining and
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, peri-urban areas, and satellite settlements. VERTICAF’s scientific contribution is threefold: (1) to develop methods for measuring verticality and its impacts in data-scarce contexts, in Africa and
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and ice nucleating particles Data analysis of observations, numerical model output and remote sensing data Tight collaboration with the project partners and participation in working meetings Teaching
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enrich high-resolution synthetic household populations for selected case-study cities by integrating micro-census, survey, and socio-metabolic data. Design and conduct empirical research, including
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project will combine plant ecophysiology, field experiments, thermal and spectral remote sensing, UAV observations, and satellite data. Initial work will take place at the Pfynwald experimental forest
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models for crack-based structural assessment - Integration of experimental data into practical engineering tools and simplified models The successful candidates will join the Concrete Behaviour and
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reinforced concrete and unreinforced masonry buildings. Our approaches include numerical and mechanical modeling, large-scale testing as well as data-driven methods. The PhD candidates will contribute to