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Commission we are looking for a Phd candidate to analyze urban trends. Using detailed spatial data, you will analyze changes in the physical manifestation of cities. So, how does the urban area develop because
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sequencing instruments into microscopes! Your mission You will join our team, a leading group in the field of DNA nanotechnology specialized in creating molecular tools for life-science research (http
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project (www.bigdatpol.com ), we are looking for a doctoral researcher with a strong interest in AI-driven analysis, modelling and decision support. About the project Crime and security constitute a
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interdisciplinary collaboration, in addition to their own specialty. Skills and knowledge related to data handling, such as the acquisition, analysis, and modeling of observational data, are highly desirable. 2
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in Urban Informatics & Smart Cities and Doctor of Philosophy. LSGI has a very strong research programme that encompasses research activities in the areas of urban informatics, spatial big data
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simulation concepts with experience in wildfire modeling and loss assessment and regional planning of communities at the wildland-urban interface (WUI). Successful candidates will contribute to the development
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side by side with private companies and actors from the healthcare sector. Several key infrastructure facilities are available, including specialized laboratories for spatial omics, mass spectrometry, flow
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 1 month ago
. Description: This opportunity is closed to applicants who are Senior Fellows (5-years or more past PhD). The Goddard Earth Observing System (GEOS), developed by NASA’s Global Modeling and Assimilation Office
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spatial and temporal data analysis using advanced machine learning technologies. The successful candidate will become a part of an interdisciplinary team working to develop machine learning techniques
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forest systems.The position offers the opportunity to work across scales and methods, integrating quantitative risk modelling with empirical field data and applied demonstrations to support climate