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to incorporate additional field data. This PhD project offers a great opportunity to work with large-scale biodiversity and climate datasets, develop strong analytical skills, and collaborate with international
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comprehensive databases combining nationwide Norwegian health and socioeconomic registry data, biobanks and patient-reported data. Using advanced epidemiological methods, causal inference and machine learning
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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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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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: 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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large datasets, and applying AI approaches (e.g. machine learning, image segmentation, multimodal AI data integration) will be considered advantageous. Strong skills in communicating scientific results
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Ine-Therese Pedersen 15th August 2025 Languages English Norsk Bokmål English English PhD position in Deep Learning for Metocean Data Apply for this job See advertisement About us We are announcing a
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interdisciplinary center with joint efforts in theory, computer simulations and experiments, both in fundamental and in more applied directions. The center works to advance the understanding of porous media by
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foundation for theory-guided catalyst design e. g. by machine learning approaches. Duties of the position Complete the doctoral education until obtaining a doctorate Carry out research of good quality within
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”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case