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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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project (Decarbonization of Heating and Cooling), we are seeking a motivated and qualified PhD candidate to design integrated district heating and cooling systems. Future thermal networks based on renewable
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personal responsibility, excellence and flexibility to work and deliver results. The candidate should have excellent written and oral communication skills in English. Knowledge of German is beneficial, but not
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. Experience in hygrothermal measurements is advantageous (temperature, infrared, anemometry). Excellent communication skills and fluency in English (both written and oral) are mandatory. Knowledge of German is
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team player and your strengths include taking personal responsibility, excellence and flexibility to work and deliver results. The candidate should have excellent written and oral communication skills in
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details of two reference persons. Please submit these exclusively via our job portal. Applications by e-mail and by post will not be considered. Where to apply Website https://academicpositions.com/ad/empa
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process optimization. Experience in hygrothermal measurements is advantageous (temperature, infrared, anemometry). Excellent communication skills and fluency in English (both written and oral) are mandatory
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Highly motivated, communicative and interested in exploratory research Our offer The work will be carried out at Empa - Dübendorf, located near Zurich, and should lead to a PhD degree from EPF Lausanne
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plus Fluent in written and oral English. Self-motivated, critical-thinking, open-minded and communicative team player Willingness to work at night for short periods of time during critical sampling