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epidemiology, causal inference, genetic epidemiology, and machine learning. As a PhD candidate in the project, you will: Actively participate in group meetings, design statistical analysis plans in collaboration
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that combine principled reasoning with the efficiency of modern machine learning to enable intelligent, real-time decision-making in large-scale interconnected systems. This position offers the opportunity
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patients Experience with clinical data collection Familiarity with epidemiological methods and registry-based research, epigenetic analyses or machine learning. Interest or experience in science
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machine learning, e.g. predicting rate of penetration (ROP) and wear. Investigate the possibilities in automation and robotization and the use of artificial intelligence. Electric drilling and other methods
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: WP1 will analyze large-scale registry data from the Nordic countries and England to assess the safety of modern ASMs. WP2 will investigate genetic, epigenetic, and pharmacological markers of ASM-related
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in crystalline rocks. Drilling optimization using machine learning, e.g. predicting rate of penetration (ROP) and wear. Investigate the possibilities in automation and robotization and the use
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of Information Technology and Electrical Engineering. Knowledge of fundamentals of C++ programming. Competence in code optimization. Knowledge of hardware/software co-design principles, and computer architectures. Good