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Field
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Postdoc: Hybrid Geospatial Modelling and Scenario Development of Carbon Faculty: Faculty of Geosciences Department: Department of Physical Geography Hours per week: 32 to 40 Application deadline
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over the course of the project. References: - Deneu B et al (2021) Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment. PLoS Comput
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for preprocessing, integration, and modeling of heterogeneous data (spatial, temporal, tabular) -Conduct research in explainable AI and uncertainty quantification applied to agronomic decisions. -Collaborate with
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homologous recombination and its intersection with the spatial organization of chromatin in the nucleus. They use high-throughput genomics approaches in the model organism S. cerevisiae. They are looking for a
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Is the Job related to staff position within a Research Infrastructure? No Offer Description The Role Applications are invited for a Postdoctoral Research Associate in the field of agent-based modelling
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augmentation for soil and biomass carbon forecasts and scenario modelling across Europe; developing and benchmarking uncertainty quantification methods for space-time predictions and for spatial blocks
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for predictive and prescriptive urban data analysis. Experience in visualizing and analyzing spatial and functional interactions within urban infrastructure. Personal and Organizational Skills Ability to model
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 3 hours ago
developing software and analytic tools to perform disease modeling and critical analytics in response to infectious disease outbreaks. ACCIDDA is part of the newly formed CDC-funded national network of centers
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practices ● Fluency in programming languages such as R and Python. ● Demonstrated ability to make reproducible code for cleaning, integrating, and modeling spatial/temporal data from multiple sources, spatial
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methodology for analysing long-term spatially structured data sets within a joint species distribution modelling framework. For more information on REC, please see https://www2.helsinki.fi/en/researchgroups