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and patient-reported outcomes; (b) observational research and comparative effectiveness studies; (c) intervention studies; (d) clinical informatics, mobile/electronic health; (e) machine learning
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perspective, including (but not limited to): machine learning and statistical learning computer vision and sensor-based data analysis natural language processing and large language models hybrid, interpretable
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management Machine learning, artificial intelligence, and big data analytics in finance Technological innovations for financial services Regulatory issues and challenges in FinTech Digital economy and
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computational focus on innovative development and application of novel data-driven methods relying on machine learning, artificial intelligence, or other computational techniques. The subject area concerns
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modelling and machine learning for large and complex datasets. Have proficiency in Python and/or R for time-series and sensor data analysis. Have an interest in or experience in environmental exposure
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. Subject matter expertise for area(s) of responsibility. PHYSICAL REQUIREMENTS*: Constantly perform desk-based computer tasks. Frequently sitting. Occasionally stand/walk, reach/work above shoulders, use a
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https://pubs.acs.org/doi/full/10.1021/acssuschemeng.5c0419 The successful candidate will be able to: Work safely and independently in a laboratory setting Learn new techniques and protocols Plan and
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the following training will be considered PhD in computer science, machine learning, AI or related computational field, or, Ph.D. in a health-related discipline with experience in experimental science, devices
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to the successful completion of a PhD degree. For more information see: http://www.mn.uio.no/english/research/phd/ All candidates and projects will have to undergo a check versus national export, sanctions and
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doctoral students. We conduct world-leading research and education in both theoretical and experimental physics, including the development and use of large-scale infrastructures. We also collaborate