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, however, these may not always be able to provide the spatial and/or temporal coverage that is required. In such cases, information from ground measurements can be combined with information from other
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., health and climate/environmental data) and could include a range of data science methods, such as utilising geographical information systems (GIS), statistical analysis, machine learning, deep learning
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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
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for estimating sustainable city size by identifying key determinants (e.g., accessibility, agglomeration, emissions, equity), testing spatial boundary sensitivity, and broadening sustainability
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experiments. Data & Analysis: • Collaborate with data scientists to analyze host and microbial data using statistical, bioinformatic, or machine learning approaches. • Contribute to the integration of spatial
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chemistry) laboratory; statistical skills for proper data treatment ; experience with programming (R or Python) for data handling and visualisation; strong analytical skills; excellent communication skills
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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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cancer using graph neural networks. Our current efforts extend this to additional cancers and modalities, such as multiplexed immunohistochemistry (mIHC), immunoflouresence, spatial transcriptomics and
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campaign. Using the Silencio app, noise dosimetry and community workshops, you will collect and analyse spatial–temporal datasets, link exposure to EU health indicators and co-produce a Local Area Noise
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flowering times over large spatial scales. Now, the PollenNet project seeks a highly motivated PhD student working at the interface of plant phenology, citizen science and ecological modelling. The PollenNet