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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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or Computer Science; Human-computer Interaction, Spatial Cognition or related areas; Engineering, Applied Mathematics, Statistics or another Quantitatively Oriented Discipline. Application procedure Your complete
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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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projects involve large-scale population cohorts, single-cell genomics, statistical genetics, functional genomics, machine learning, and clinical translation. We are a diverse and international team
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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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PhD position in environmental toxicology and endocrine disruption: Focus on new endpoints in zebr...
organism Proficiency in laboratory techniques, including OECD fish test protocols, histopathology analysis, and behavior analysis. It is an advantage if the candidate has experience in statistical analysis
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, mathematics, biology, and epidemiology, developing and applying novel statistical methods and deep learning approaches for global health challenges. The group’s research spans disease modelling, genomic
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Science, and other research sections at the department are Algorithms Computational Science Data Science and Statistics Geometry, Topology and Algebra Learning Experience Design Our department offers
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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