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Postdoc in statistics to develop Bayesian privacy metrics for synthetic health data (2024-224-05725)
on Bayesian statistics and apply them in several real-world settings of important clinical relevance. The postdoc will be responsible for developing the area with a group consisting of a PhD student, a data
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parameters. The model is used for condition monitoring and fault detection using methods focusing on statistical methods using residual generation and Kalman filtering. Qualifications: Phd and master's degree
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science, statistical analysis, and mathematics. Experience with programming languages such as MATLAB and Python. Excellent analytical, problem-solving, and modeling skills. Strong written and verbal
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