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exposome and dynamic exposome modeling, learning in timeseries and spatial data, and hybrid deep learning-causal modeling. The successful applicant should have significant research experience in at least two
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spatial omics datasets. The position will also contribute to multi-modal data integration efforts that combine imaging, genomics, and machine learning approaches. Key Responsibilities Data Processing
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-scale job ads datasets, spatial datasets, patents). Conduct data analysis using econometric and statistical tools. Excellent knowledge of R is expected. Good knowledge of Python, experience with modern
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characterization of deep-water habitats, GIS spatial analysis of species distribution data, and quantification of ecosystem services. Preference will be given to applicants that possess a diverse set of skills and
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About the Role Join a multi-disciplinary team that explores human intestinal development and disease using cutting edge single cell and spatial biology technologies, organoid models and
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spatial analysis of species distribution data, and quantification of ecosystem services. Preference will be given to applicants that possess a diverse set of skills and can contribute to more than one
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piezoelectric composites and material architecture to achieve improved electromechanical coupling and spatial resolution would be important. In addition, they should have experience in sensor array readout
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the context of both demand and supply to promote sustainable built environments. Building Environment: Optimisation of buildings and built environments for thermal comfort, indoor air quality, visual / spatial
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automatization. In discussion with the successful candidate, they will choose further research areas within our group, such as enhancing the spatial and temporal resolution of the measurements, and integrating
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requirements are Experience in working with large-scale spatial-temporal traffic and/or travel behavior data, e.g., loop detector, floating car data, GPS data, cellphone data. Experience with transport