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Field
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accessible measure of location. One option is to use skew versions of know distributions that leave the mode or median of the distribution at the origin. The main objective of this project is to explore, from
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, and hybrid models integrating computer-vision–derived features. Build and test pipelines for pose detection, object tracking, optical-flow analysis, and gaze–scene alignment, in collaboration with
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complex, multi-objective optimization problem. It involves searching over materials, geometries and fabrication parameters to balance competing objectives such as coherence time, anharmonicity and
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Two-year postdoc position (M/F) in signal processing and Monte Carlo methods applied to epidemiology
. Automated data-driven selection procedures will enable to gain objectivity and capacity to handle large amount of data from a wide range of epidemics. The first challenge consist in refining previous models
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, Bayesian inference, and decoding/encoding methods. (Optional) Conduct in vivo calcium imaging experiments in C. elegans to study how neural circuits generate behavior. Engage in creative, hypothesis-driven
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, custom-trained neural networks, and related tools. Analyze and interpret high-dimensional neural datasets using systems neuroscience approaches such as neural networks, Bayesian inference, and decoding
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tasks require high-frequency evaluations of forward models, in order to quantify the uncertainties of rock and fluid properties in the subsurface formations. Therefore, the objectives of this research
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-represented backgrounds. The objective of the research project is to perform Bayesian inversion to characterise the velocity field of 3D partial differential equations describing brain fluid and solute movement
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Research Engineer/Postdoctoral Position Decision and Bayesian Computation (DBC) – Epiméthée (EPI) Laboratory Institut Pasteur, Paris | 25 rue du Docteur Roux, 75015 Paris Position Overview We
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uncertainty from climate projections into land-use forecasts. Advance Bayesian and ensemble learning approaches for non-stationary temporal processes. Implement probabilistic diffusion or generative models