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closely with data scientists to interpret and predict MFA data using nonlinear reaction-diffusion models, 13C-isotopomer analysis, and MATLAB-based simulations enhanced by Bayesian Machine Learning
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the area of inference, information build-up and learning methods in the general context of the project. Apply established techniques and develop new methods inspired by Bayesian methods and statistical
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record in the development of multi-channel digital signal processing in optical communications, and particularly the compensation of non-linear effects from all sources. An ability to learn new techniques
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