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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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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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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
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of optogenetic systems to activate TBRA in specific cellular subpopulations. 2. Characterization of phase transitions • Analysis of EMT and mesodermal markers: Immunofluorescence to track key markers (TBRA, Snail
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well as potential power deposition on accelerator elements and potential damage. •Subsequently perform the benchmarking of the accelerator model from the Bmad code to the Xsuite / RF-Track codes. This work will
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and as part of a team ; - Strong scientific communication skills (oral and written) ; - A track record of research excellence, evidenced by publications in peer-reviewed journals. The candidate must
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will be measured at the same time to find specific points where the beam polarization is significantly degraded. Regular measurements will thus be carried out to track beam energy fluctuations