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development. It will involve a paradigm shift which combines geo-spatial-temporal modelling, prospective life cycle analysis, techno-economic assessment and AI methodologies. It will map and analyse
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. This data will be used as input in climate models to ultimately propose a spatial characterization of bike paths. The methodology will be tested in several areas in France and Switzerland and discussed with
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research in the lab, please look at our lab publications: https://www.ncbi.nlm.nih.gov/myncbi/1ZuZJziC0nrUgr/bibliography/public/ Includes a terminal degree (PhD). Prior experience working with Mouse and
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the Department of Human Geography and Spatial Planning (max. 10% of the appointment). Where to apply Website https://www.academictransfer.com/en/jobs/358070/phd-the-urban-ocean-nexus-towar… Requirements Specific
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grasslands and evaluation of land-use intensity, Expertise in classification with machine-learning methods, statistics, spatial analysis and land-use modeling, Experience and interest in conducting fieldwork
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Pytorch and/or JAX deep learning models. Experience in single-cell or spatial omics data analysis. What we offer Embedding within a computational team, with extensive experience in computational biology and
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Pytorch and/or JAX deep learning models. Experience in single-cell or spatial omics data analysis. What we offer Embedding within a computational team, with extensive experience in computational biology and
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process models from the field of spatial statistics to model clustered patterns across the landscape, and develop methods for estimating plant population size and/or change. Qualifications: Requirements
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carried out include the pre-processing of spatial and temporal data and the implementation of Machine Learning models for the classification of fishing activity. The grant holder will also support the
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knowledge needed for testing of ecological hypotheses, performing bioeconomic analyses, and informing science-based policymaking. Where to apply Website https://www.academictransfer.com/en/jobs/359448/phd