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, Mechanical or Electrical Engineering, Control, Applied Mathematics, or a related field. You should have a solid foundation in machine learning and strong programming skills (Python, deep learning
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programs in parallel. Please check this page for additional information. Please note that this is a separate application process necessary to be eligible to complete your PhD at EPFL. Potential doctoral
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Diplomas Transcripts/grades from Bachelor’s and Master’s studies If you are successful, you will need to enrol in one of the EPFL doctoral school programs in parallel. Please check this page for
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areas. ● Strong computational/programming skills (Python/C/C++) including computational mechanics, and scientific machine learning. ● Excellent English communication skills (oral and written). We offer
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scientific research independently Basic knowledge of scientific programming Data handling skills Good interpersonal skills, team player Good knowlege of English (written and spoken) We offer A dynamic
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PhD Student Position in Interoperable Digital Workflows for Regenerative Building Design and Perform
models, simulations, environmental databases, sensors, surveys, and interviews Experience in scientific programming, scripting, or workflow automation. Knowledge of Python or an equivalent language is
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Distributed Fibre Optic Sensing - Solid analytical and problem-solving skills - Programming experience (e.g., Python, MATLAB) and experience operating on large datasets - Ability to work both independently
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. Technical Skills Proficiency in remote sensing (satellite image processing, GIS, photogrammetry); Programming skills for geospatial analysis: Python (rasterio, geopandas, scikit-learn) or R
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interest in sustainability transitions and/or urban systems tipping dynamics. Good knowledge of computational modelling and programming. Experience with Python and agent-based modelling is particularly
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in at least one programming language for geospatial analysis: Python (rasterio, geopandas, scikit-learn), R or Google Earth Engine. Experience with open-source tools (QGIS, GRASS GIS, GDAL) and