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machine learning methods to monitor CO2 and NOx emissions using the upcoming satellite missions (e.g., CO2M, TANGO, Sentinel-4/5). Your research will contribute directly to monitoring global efforts
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of Zurich and Wageningen University & Research. The four-year STEPS project focusses on developing data-driven and machine learning methods to monitor CO2 and NOx emissions using the upcoming satellite
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-world energy applications, the project aims to better capture the dynamics of urban infrastructures across different spatial and temporal scales, from building-level energy demand to district-scale
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-world energy applications, the project aims to better capture the dynamics of urban infrastructures across different spatial and temporal scales, from building-level energy demand to district-scale
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energy system models that incorporate a stronger Social Sciences and Humanities (SSH) perspective. By embedding societal dynamics, such models aim to capture a wider range of future uncertainties and