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in environmental health and ecology as well as working with motivating datasets to become familiar with the data structure, challenges and competing methods. This position requires a Phd degree in
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with physicians to analyze patient scan data and optimize MR techniques to meet evolving clinical needs. 4. Managing technical implementation, optimizing scan protocols, and ensuring timely delivery and
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will develop novel statistical and machine learning methods for any of the following: multi-omics data (such as bulk and large-scale single-cell RNA sequencing data, spatial transcriptomics, bulk and
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statement outlining potential mentors and research programs from the list of faculty mentors. More information about the program and the list of faculty mentors can be viewed at: https://medicine.duke.edu
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. The Postdoctoral Associate will apply his/her technical skills toward development and implementation of machine learning, computer vision, and other algorithms for analysis of medical images and prognostication as
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: • Experience with geochemical speciation methods, particularly synchrotron X-ray spectroscopy • Knowledge of data management • Experience with statistical analysis of data and geospatial analysis tools (e.g
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experimental procedures. Data preparation for oral presentations, grant applications and publication in professional journals. Implementation of bioinformatics approaches. Conformance to standards of responsible
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ecologists at the Nicholas School of the Environment. The position provides full benefits and a competitive salary. Candidates should provide 2 reference names and contact information and a CV. They can apply
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of output from global climate models (CMIP-class models) as well as Integrated Assessment Models (IAMs) such as GCAM or PAGE. The candidate must have a PhD degree in a related field, be fluent in computer
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identity, genetic information, national origin, race, religion, sex, sexual orientation, or veteran status. Duke aspires to create a community built on collaboration, innovation, creativity, and belonging