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Vision Profiler (UVP), and to analyse its spatial and temporal variability. This will be done by combining different data sources and machine learning (ML). Data used for this ML approach include - a
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such as artificial intelligence, geographic information systems, and statistical methods. The researcher will be responsible for: Helping collect, organize, and ensure interoperability of clinical
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inference, bias mitigation, and statistical modelling. Expertise across diverse epidemiologic methods and content areas is welcomed. Develop a research program that incorporates rigorous epidemiologic methods
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areas that advance geospatial concepts and theory, including: spatiotemporal modeling, Natural Language Processing implemented within the spatial domain, Big Data cloud computing for spatial statistical
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- statistical techniques used in spatial data analysis - scientific programming, e.g. Matlab, R, Python or Julia - designing and/or conducting field measurement campaigns in atmospheric
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inference and prediction of gas dynamics at high spatial and temporal resolution, and in turn more effective climate change mitigation, urban air quality management, and rapid response to hazardous releases
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agricultural science with a quantitative focus (or an equivalent discipline) expertise in statistical and machine learning approaches, with the ability to apply advanced methods to complex environmental and
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, addressing key questions of coherence, entanglement, and other forms of non-classicality while developing practical free-space quantum links. Using spatial light modulators and adaptive optics, we will
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the job funded through the EU Research Framework Programme? Horizon Europe Is the Job related to staff position within a Research Infrastructure? No Offer Description The Centre d’Études Spatiales de la
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lack reliable uncertainty quantification. The methods developed in the project will tackle these shortcomings, enabling computationally efficient inference and prediction of gas dynamics at high spatial