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Field
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part of our team, you will leverage state-of-the-art single-cell, spatial and organoid technologies in a collaborative and interdisciplinary environment. Research focus We welcome candidates with a
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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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Postdoc in modelling Greenland and Himalaya precipitation using machine learning Faculty: Faculty of Science Department: Department of Physics Hours per week: 36 to 40 Application deadline: 26
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Postdoc: Hybrid Geospatial Modelling and Scenario Development of Biomass Faculty: Faculty of Geosciences Department: Department of Physical Geography Hours per week: 32 to 40 Application deadline
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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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) and spatial Machine Learning (ML) models Salary and full employee benefits are offered in accordance with Princeton University guidelines. The Term of appointment is based on rank. Positions
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tools, data science techniques, and spatial analysis to leverage big data from cities and model their evolution. Main Tasks and Responsibilities: Collect, clean, and structure large volumes of urban
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applicant for this job will conduct applied research in the areas of transportation, traffic-related air pollution, human exposure assessment and public health impact modelling. Part of the work will be
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an industry partnered project for translational drug discovery. The role will involve analysing large scale omics and spatial datasets from both primary patient samples and advanced in vitro model systems
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an industry partnered project for translational drug discovery. The role will involve analysing large scale omics and spatial datasets from both primary patient samples and advanced in vitro model systems