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Field
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energy data lifecycle spans pre-construction (e.g., meteorological mast data, LiDAR data, wind climate and energy yield modelling, environmental impact assessment data), operational phases (e.g., SCADA
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mixed research methods—including behavioural surveys, environmental monitoring, and dynamic thermal modelling—the project aims to generate retrofit strategies that improve energy efficiency, reduce carbon
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/or modelling is essential. Experience in machine learning, computer vision, and computer programming is desirable. In addition, applicants should be highly motivated, able to work independently, as
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actors involved in managing public space? How can they adapt to dynamic societal needs and increasing spatial interdependencies? What institutional models promote more efficient, equitable and resilient
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/emissions modelling preferred but not required Creative problem-solving skills and ability to work independently *Candidates with a PhD in other disciplines may be eligible if they can demonstrate exceptional
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adverse consequences for overall thermal efficiency and component life (environmental impact, sustainability and safety). While standard analytical models for heat transfer exist for classical cases
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season properties (e.g. number, intensity) for lead times ranging from one to approximately six months in the latest generation of dynamical seasonal and decadal forecast models. Seasonal forecasts
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problems in health data science. Air pollution is composed of several different environmental pollutants, for example particulate matter (PM10 and PM2.5), ozone (O3), nitrogen dioxide (NO2) and sulphur
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. Empa is a research institution of the ETH Domain. Empa's Laboratory of Biomimetic Membranes and Textiles is a pioneer in physics-based modeling at multiple scales. We bridge the virtual to the real world
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We are looking for a PhD candidate fascinated by the response of quick clays that can change from solid to liquid with small environmental perturbations. We want to be able to understand how a