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experience in life cycle assessment (LCA) and related tools for managing large data sets to evaluate natural resources needed to advance emerging technologies. The candidate will lead their primary project and
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outside Duke University. Preferred qualifications: PhD (completed in the last 1-5 years or PhD candidate) in a quantitative discipline, including Computational Biology, Bioinformatics, Computer Science
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Assessment Models (IAMs) such as GCAM or PAGE. The candidate must have a PhD degree in a related field, be fluent in computer programming, preferably python, and will ideally have experience in working with
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for this position will be a highly motivated individual with experience in deep learning and medical imaging and a PhD degree in computer science, electrical and computer engineering, biomedical engineering
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Preferred Qualifications: A PhD or MD/PhD (or equivalent) in biological sciences (cell & developmental biology or a related field) which was awarded not more than 18 months ago. Evidence of successful
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of Civil and Environmental Engineering / Chaney Lab: Perform the core of the proposed research activities including processing the remotely sensed LST to compute the spatial statistics, run the HydroBlocks
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) and bioinformatics tools Familiarity with data science, machine learning, artificial intelligence, natural language processing and applications to electronic health records and big data and
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, Duke University Biology Department to study how archaeal microbial communities respond to stress in hypersaline environments. A PhD in computational and/or experimental biology is required in fields
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-of-the-art IT infrastructure. Ideal candidates should hold a PhD in the area of Computer Science or Electrical and Computer Engineering and have strong programming skills including Python. Past work in
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animal species, generating standardized data that works effectively across diverse languages and cultural contexts while eliminating traditional barriers of recall bias. These methods are being deployed in