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chemistry, spectroscopy, and computation to iterate design–build–test–learn cycles and to elucidate structure–activity–mechanism relationships. You will join a well-equipped laboratory and a professional
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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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outages and degradations become more severe. Technical failures, unplanned excessive load, design and implementation flaws in components, human error, and malicious intent all have the potential
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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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period During the first months of the appointment, the successful candidate is expected to prepare a plan for the progress of the work towards a PhD degree, with a view to completing a doctorate within the
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Package (WP). This research aims to leverage advancements in sensing and ubiquitous systems to address how physical, spatial, and social design opportunities (e.g., through interactive functionalities and
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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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(written and spoken) The successful candidate is expected to move to Bergen Desired Qualifications: Experience with Stata Experience with causal inference methods, survey design, and/or machine learning
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3rd August 2025 Languages English English English The Department of design has a vacancy for a PhD Candidate - Netnography-Driven User-Centered Design for the Green Energy Transition Apply
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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