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, sampling, inference, and machine learning. On one side, statistical approaches such as Bayesian inference play a critical role in identifying the parameters of PDEs, while on the other, newly emerging
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. Knowledge of ensemble reweighting techniques (e.g., Bayesian approaches, metainference) and the ability to assess model-to-data fit quality. Proficiency in Unix/Linux environments and solid programming skills
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* knowledge of machine learning techniques for data analysis * proven scientific track record and experience in interdisciplinary research * English at a communicative level, assessed based on an interview
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independently in an interdisciplinary team have a track record of scientific achievement (possibly documented by publications in peer-reviewed journals) Specific Requirements experience in fluorescence microscopy