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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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in person in São Paulo and involves single-cell and spatial transcriptomics analysis, integration of multi-disease datasets, investigation of cell–cell communication, and the development
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 2 months 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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areas of sustainable intensification of agriculture, climate mitigation and adaptation, livestock systems, and healthy and sustainable diets, providing analysis and insights aimed at helping to shape
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themes: development of innovative tools for single-cell and spatial analysis of lipid immunometabolism (Subprojects 1–5); molecular pathways linking lipid metabolism to immune activation and
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: Contrat doctoral PHD Country: France Where to apply Website https://www.abg.asso.fr/fr/candidatOffres/show/id_offre/135353 Requirements Specific Requirements Master degree required, in Fluid Mechanics
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these skills. Competence in data processing, statistical analysis, and the development of ML models, or a clear willingness to gain such expertise. Experience with GIS-based spatial analysis, or a clear
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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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Multisport card Selection process Interview via Zoom with selected candidates. Website for additional job details https://www.mff.cuni.cz/en/physicsphd/f8/phd-projects-funding/internal-climate-… Work Location
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
solving complex inverse problems that link measurements to their underlying causes. This PhD interdisciplinary programme focuses on Bayesian methods for estimating physical parameters from high-dimensional