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Field
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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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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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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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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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) to monitor and improve public health. You will contribute to modeling smart cities with a focus on health and designing decision-support tools for sustainable and inclusive urban environments. Key
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molecular mechanisms driving lymphoma progression and therapy resistance. This position will leverage cutting-edge approaches including spatial transcriptomics, CRISPR-based functional genomic screening, and
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including cell culture, organ-chip models, tissue engineering, and musculoskeletal biology. The PDRA will plan and conduct experiments, generate high-quality data, prepare publications, make presentations and
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of techniques for countering information maneuvers, differences in online harms and influence campaigns across countries, geo-spatial differences in online harms. Successful applicants will need experience in
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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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resolution Utilize cutting-edge techniques including microelectrode arrays, microfluidics, animal behavior and spatial transcriptomics under infectious conditions Bridge hamster and human biology to find common