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YOUNG RESEARCHER IN THE FIELD OF EARLY DETECTION OF THE HEALTH STATUS OF PLANTS USING REMOTE SENSING
Mathematics » Statistics Geosciences » Other Technology » Remote sensing Engineering » Agricultural engineering Computer science » Programming Computer science » Informatics Computer science » 3 D modelling
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QUALIFICATIONS PhD in Ecology, Geography, Environmental Science, or a closely related discipline. Demonstrated specialization in remote sensing and spatio-temporal environmental data analysis. Strong programming
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neurovascular research in close collaboration with clinical teams. Main responsibilities and duties: Coordinate and manage activities within the neurovascular research laboratory Provide support to PhD students
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the field to inform farmers of varying soil conditions through traditional remote sensing techniques. For further Information, please email p.stowell@sheffield.ac.uk or look at the University’s website: https
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to apply Website https://jobs.vhir.org/jobs/7365438-postdoctoral-reseaarcher-liver-diseases Requirements Research FieldBiological sciences » BiologyEducation LevelPhD or equivalent Skills/Qualifications
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wireless communications systems. For details, you may refer to the following: https://wwwen.uni.lu/snt/research/sigcom We’re looking for people driven by excellence, excited about innovation, and looking
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relevant to the position. Research Fellow I: PhD from an accredited institution in Agronomy, Crop Science, Soil Science, Agricultural Engineering, Data Science, Remote Sensing, or a closely related field; no
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observed pipe failures, and interpreting indirect observations from remote sensing platforms such as drones and satellites. Another challenge is linking these observations to physical processes affecting
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spaceborne remote sensing. You will first identify large-scale drivers of compound extremes in models and observations, then build an emulator using advanced AI methods, such as convolutional neural networks
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large environmental and ecological data sets, incl. empirical, remote-sensing and simulated model data knowledge in programming languages (UNIX, C, or similar) and script-based programming (R, Python