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the development of headache, and how headache impacts the Norwegian economy. The project applies advanced methods in epidemiology, causal inference, genetic epidemiology, and machine learning. As a PhD candidate in
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inference methods, survey design, and/or machine learning Experience with web scraping and API-based data collection Organizational and coordination skills, such as assisting in drafting terms of reference
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the application of rock physics models, Bayesian inversion methods, and machine learning algorithms in the electromagnetic context. Qualifications and personal qualities: Applicants must hold a master’s degree (or
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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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for assessment prior to the application deadline. It is a condition of employment that the PhD has been awarded. Applicant should have a genuine interest in AI the learning sciences, and the research proposal must
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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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for the Science of Learning & Technology (SLATE), Faculty of Psychology there is a vacancy for a postdoctoral research fellow position within artificial intelligence and education. The position will also
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registry-based research, epigenetic analyses or machine learning. Interest or experience in science communication and public engagement Experience with publishing biomedical papers Experience with open
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/Machine Learning (AI-ML) approaches to meeting this challenge. Possible topics include, but are not limited to: storylines for plausible narratives of regional climate change, novel algorithms for rare
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will be adapted to the candidate’s background and the evolving needs of the center. Possible directions include the application of rock physics models, Bayesian inversion methods, and machine learning