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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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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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science(e.g., mathematics, physics, computer science, etc.), a strong background in data science, statistics, or machine learning, and an interest in biomedicine. Alternatively, a candidate with a
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on generative AI have been suggested. The data science team under this project will develop privacy metrics based on Bayesian statistics. The overarching purpose of the legal part of the project is to develop