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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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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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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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. Empa is a research institution of the ETH Domain. Your tasks Optimizing vehicle aerodynamics to reduce transportation emissions, understanding airborne disease transmission, and predicting climate
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, investigations and optimization of hydrogen production via methane pyrolysis for decarbonization of industrial high-temperature processes with potential for negative carbon emissions. Your tasks Setup
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reproducibility and scalability; modify surface chemistry for biocompatibility and stability. Formulate and optimize solution: Integrate nanozymes into a clinically relevant solution formulation; characterize
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: Ensure reproducibility and scalability; modify surface chemistry for biocompatibility and stability. Formulate and optimize solution: Integrate nanozymes into a clinically relevant solution formulation
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knowledge and understanding in pyrolysis processes we are looking for a PhD student for scientific analysis, investigations and optimization of hydrogen production via methane pyrolysis for decarbonization
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Network with 15 funded 3-year PhD positions in parallel. Your profile Master Degree in environmental/natural sciences or engineering, or similar. Experience with developing computational models Preferably
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and mateRials, fabrication techNologies for the responsible co-creation of future Sustainable integrated electronic systems), an EU MCSA Doctoral Network with 15 funded 3-year PhD positions in parallel