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spatial, temporal, or spatiotemporal modeling, process-informed spatiotemporal models, causal inference, AI/ML/DL, causal ML, explainable ML, AI/ML/DL, and other relevant analytical skills. Experience
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. In addition to furthering systematic thinking by separating problem formulation from solution concepts, integrating models from different disciplines, operating at different time/spatial scales
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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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will seek to understand the social conditions under which large-scale changes in norms and behaviours around accessing mobility on demand emerge. Our models will consider factors relating to social
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analysis of spatial-omics and imaging data, developing a framework for simulations, simulating epidemics on social networks, building generative models to improve computation time for simulations, and other
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. Research tasks: We are looking for enthusiast candidates who can strengthen the department’s research capacity in the field of spatial analytics and geospatial modelling. Thematically, the research should
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of interactive web-based and/or immersive analytics environments that integrate temporal networks, heterogeneous spatial-temporal data, and AI-driven forecasting and simulation models. These environments will
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with The School of Natural Sciences and the Discipline of Geology, seek to appoint an AIB/E3 Assistant Professor in the area of Earth System Modelling. More specifically, the successful candidate will utilize
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developing mathematical modelling and observational data analysis using high spatial, temporal and spectral resolution state-of-the-art solar telescopes (e.g. SST and/or the 4m aperture DKIST). There is also
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), (c) estimation methods for latent variable models (e.g., two-step approaches or approximate maximum likelihood estimation), or (d) meta-analytic models to address complex data structures (e.g., spatial