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driven methods. The Post Doc will be working with the latter. The model structure will be founded on first principles models with parameters that ideally possess physical significance. Parameters
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may be invited to an interview and/or asked to give a trial lecture. When the employment process has been terminated, a final rejection will be sent to the applicants who are not considered
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Postdoc in statistics to develop Bayesian privacy metrics for synthetic health data (2024-224-05725)
. Qualification requirements: You should hold a PhD degree in computer science, statistics, physics, mathematics, engineering, or a field of science relating to data science with emphasis on statistical
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enabling recycling and reuse of fibre reinforcement materials from end-of-life composite structures. We will cover conceptual design, process modelling, experimental characterization, as well
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, we strive for diversity in nationality, culture, and gender, success as a team effort, maintaining an inspiring physical work environment, and ensuring a good work-life balance. The position is within
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to comment on their own assessment, and may be invited to an interview and/or asked to give a trial lecture. When the employment process has been terminated, a final rejection will be sent to the applicants