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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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the services nature provides to people. The position will combine ecological data analysis with statistical and spatial modeling to quantify chemical impacts across multiple levels of biological
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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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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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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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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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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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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
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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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- 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