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Description Professor Julia Cagé, Principal Investigator of the European Research Council consolidator grant No. 101231066 ECOSOCIAL: Elections, Ecological Inference and Social Capital in Historical Perspective
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from ground or space. The work will consist of continuing to test and process data for inferring information on non-homogeneous aerosol model in the GRASP retrieval algorithm in order to improve
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proposes to cross-correlate thermal proxies, including changes in thermal buoyancies recorded in the elevation history, with existing and newly acquired temperatures inferred from mafic magma reservoirs
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and implement Bayesian graph neural networks and convolutional neural networks as surrogates for high-fidelity biomechanical models Quantify and propagate uncertainty, and develop strategies for model
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candidate, with a strong background in the development of machine learning methods for bioinformatics. The project focuses on the development of new neural network architectures to perform inference
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/landscape openness/deforestation), sedimentology analysis for reconstruction of past human occupation and pollution, and charcoal analysis for inference of past fire history, metallurgy, and land-use
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(causal inference, prediction, performance...) but you will be mentored by a senior statistician on site and several data-scientists Speaking French is not mandatory but will facilitate integration
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to irrigate engineering processes and validate this design as early as possible; (P2) integrate a reliable and frugal embedded AI (limited to inference and/or also capable of learning, e.g. reinforcement
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energy efficiency during the training process and (ii) science yield (sensitivity / resolution) during inference. A key aspect is to benefit from hybrid HPC + AI approaches within the workflows
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modeling and simulation, and statistical inference (lead by mathematicians and biologists) - The recruited postdoc will be asked to work in the labs on a daily basis. - The recruited postdoc will be expected