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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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Two-year postdoc position (M/F) in signal processing and Monte Carlo methods applied to epidemiology
. To that aim, both Stein-based bilevel optimization, empirical Bayesian and unsupervised deep learning approaches will be considered. The recruited postdoc researcher will tackle both implementation challenges
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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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in the use and exploitation of high spatial and temporal resolution remote sensing data. An interest in causal discovery and inference is more than welcome. The candidate will be required to interact
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inference, experience using HPCs, master student supervision experience, github. Also would be an advantage: experience working with clinical scientists, understanding of ALS physiopathology, decent level of
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on the statistical inference of Linear Mixed Models (LMMs). The project's goal is to develop a new breed of Mixed Effects Neural Networks (MENN) for Genome InterpretationI that take the best from both worlds, merging