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, statistics, data science, and public health. The goal is to develop new methods that allow researchers to learn from sensitive health data without compromising individual privacy. Using unique, nationwide
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research questions, and be able to think critically and develop your own scientific ideas. Previous experience with statistical analysis, programming (e.g., R or Python), machine learning, or genomic data
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statistical modeling with clinical insight, aiming to improve risk prediction and inform sex-specific prevention strategies in atrial fibrillation patients. The research will be conducted in close collaboration
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for efficiency monitoring and fault detection, combining sensor data, system layout knowledge, and physical principles to extract spatial-temporal features and predict equipment behaviour; 2) Statistical anomaly
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analyze temporal dynamics using time series methods and statistical techniques, and you will explore spatial variability using suitable modelling and data analysis approaches. A central task will be
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mechanisms such as fatigue, fracture, ultimate strength, biofouling, corrosion, etc. Assess the metocean statistics to obtain 50- and 100-year return period load combinations. Develop and document high
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degree in computer science, mathematics, statistics, physics or relevant fields. Strong background in machine learning, preferably experience in probabilistic modeling, Bayesian machine learning, or graph
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analysis in software such as R, Stata, Python or similar tools. You are comfortable working with datasets, developing statistical models and documenting your analytical choices. Experience with experimental
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primarily be using registry data from Statistics Denmark. Over the three years of employment, you will be required to deliver 600 hours of teaching and attend 30 ECTS of PhD level training. Your competencies