4 parallel-computing-numerical-methods "DIFFER" Fellowship positions at Cranfield University
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environmental monitoring. You will develop and apply state-of-the-art deep learning methods for land cover classification and change detection using multi-source aerial and satellite imagery. Working within an
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and stability of organic carbon within soil parent materials. The role will involve applying and interpreting analytical and isotopic approaches, including radiocarbon methods, to distinguish petrogenic
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research in plant and soil science using both qualitative and quantitative methods. You will have some experience in mathematical modelling and basic-level programming. But our main requirement is
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- JD & Person Spec.pdf (PDF , 106.15kb) Role Description About the Role We are looking for an enthusiastic researcher to develop advanced modelling and optimisation methods to support the transition