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5639. Qualification requirements: Appointment as Postdoc presupposes scientific qualifications at PhD–level or similar scientific qualifications. Your background is in statistics, computer science
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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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range of mathematical disciplines and has three study programmes: mathematics(including a specialization in statistics at the master level), mathematical economics, and mathematics-technology. The
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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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relevant Master’s programme, such as Signal processing, Machine learning, Statistics, Mathematics, Acoustics, or similar. The integrated stipend consists of two parts, A and B. During part A you are enrolled
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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