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integrates machine learning and statistics to improve the efficiency and scalability of statistical algorithms. The project will develop innovative techniques to accelerate computational methods in uncertainty
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and grant applications, including detailed statistical model power analysis, missing data and safety analysis; and supervising data collection and verification procedures across multiple platforms
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University Hospital, Duke Regional Hospital, Duke Raleigh Hospital, Duke Health Integrated Practice, Duke Primary Care, Duke Home Care and Hospice, Duke Health and Wellness, and multiple affiliations
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project TARGET-AI will bring together expertise from multiple research groups to advance the state-of-the-art in combining the most advanced techniques from deep learning/AI with rigorous statistical
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attachment. Main tasks Collect, compile, and analyze data to map the responses of plants and pollinators to climate change Participate in the development and adaptation of statistical models for analyzing
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similar field; expertise in programming skills and statistical data analyses, including machine learning; affinity with environmental exposure modelling and high-performance computing; strong reporting and
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an enthusiastic and collaborative colleague and meet several or all of the following criteria: a Master's degree in Data Science, (Computational) Epidemiology, (Medical) Statistics, or a similar field; a strong
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Responsibilities for the role include: Data collection, cleaning, and merging from large-scale microdata sources (e.g., patents, dissertations, bibliometrics). Conduct data analysis using econometric and statistical
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Solutions) and about how to scale them up. The ambition of NL2120 is to stimulate the use nature (Nature Based Solutions) as a solution for social challenges, such as climate adaptation and multiple claims
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within a BSc/MSc thesis project. · Ability to effectively and reliably coordinate with the supervisor and multiple collaborators Language skills · Good level both written and spoken