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) To develop Deep Learning algorithms to significantly speed up probabilistic inference algorithms of current spatial birth-death models 2) To incorporate fossil stratigraphic and spatial information into a new
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is to provide the most **informative** samples (not just the most probable) to facilitate identification and enhance user experience. - **Theoretical Model Improvement:** Understanding a sample's
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researchers addressing complementary topics and methodology such as Thermodynamic modelling of multi-component planetary degassing/ingassing, Molecular Dynamic simulations of silicate melts, Petrology
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., topic modeling, transformer models). Experience with historical or cultural datasets. Competence in a relevant non-Western language (e.g., Farsi, Hindi, Japanese) or willingness to acquire basic
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project based on a living model. Consequently, working hours will be adapted to the biological requirements and developmental stages of the model organism. The scientific environment at the Institute
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candidate will be responsible for analyzing and modeling rich and complex datasets from large-scale electrophysiological recordings (Neuropixels probes) in multiple regions of the frontal cortex and striatum
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these results to initiate a model assessing the potential impacts of the mussel on the functioning of Lake Geneva. Most of the project team is based in Thonon-les-Bains at INRAe's hydrobiological station. The PhD
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element of embedded intelligence lies in the sensor's ability to self-calibrate and, in particular, to adapt its responses and models according to sensor aging and the (sometimes significant) variability
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of transportation and energy. Its experimental and numerical modeling activities focus on various complex and multiphysical flows, including turbulence, two-phase flows, combustion, and thermoacoustics. Research
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and model their variation and evolution, so that an expert can more easily analyze them. The key concept it will develop is the one of visual structures. Their key features will be