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test a conceptual SDI model for integrating heterogeneous (conventional geospatial, environmental, behavioural, and human-centred) datasets, including qualitative and quantitative data, across spatial
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. We apply these advanced imaging technologies to clinically meaningful model systems in order to evaluate their robustness, scalability, and added value for translational research. By combining super
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and conduct complex in vivo and ex vivo experiments focused on fibroblast biology, ECM remodeling, and tissue mechanics using advanced mouse models. Apply histological, imaging, and molecular approaches
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interpretable framework for tensor analysis. Specifically, the project will: Develop novel, modular statistical solvers to integrate domain-specific knowledge directly into latent variable models. Account for
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experience working with spatial data. Experience meteorological and pollutant transport models. Familiarity with measurement methods in atmospheric sciences (e.g. lidar, particle counters) A valid category B
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reached. However, this macroscopic point-of-view ignores the nanoscale spatial heterogeneity of the temperature distribution while strong thermal gradients could induce localized therapeutic effects
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combines state-of-the-art computational multiscale modelling (using DFT/TDDFT methods, collision theory, molecular dynamics, stochastic dynamics, Monte Carlo and analytical methods) and its thorough
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sponsorship Salary: Salary commensurate with education and experience Restricted: Yes Criminal Background Check: Yes About the Department: The Center for Spatial Information Science and Systems (CSISS) is an
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University of California, San Francisco | San Francisco, California | United States | about 1 month ago
compensation, please visit: https://ucnet.universityofcalifornia.edu/compensation-and-benefits/index.html Department Description The Anatomy Department is a basic science department within the School of Medicine
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. • Design and implement studies on themes such as recreational quality, mental well-being, social connectivity, and resilience—integrating geospatial and spatial-temporal analysis to assess patterns