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intelligence Sensor integration and fusion for distributed simultaneous localization and mapping (SLAM) Machine perception for object detection, identification, and tracking Test, Evaluation, Verification, and
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approaches. Experience in programming in R, using GitHub, and doing Bayesian statistical analyses with the use of MCMC samplers such as JAGS, STAN, or NIMBLE. Point of Contact Justina Eligibility Requirements
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skills: Demonstrated experience in modeling and applied statistics including machine learning, Bayesian statistics, multivariate statistics, model assisted estimation, rarefaction, or wildland fire
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received within five years of the appointment start date. Preferred skills: Candidates with a track record of publication in peer-reviewed journals, grant writing, and delivering oral presentations
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identification skills, previous waterbird or wetland field experience, banding or tracking studies, spatial modeling, writing or editing scientific papers, and running basic statistics are desirable. The most
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opportunity to explore cutting-edge techniques such as turbulence modeling, interface tracking, and solution acceleration, which are relevant to both riverine and broader fluid dynamics applications
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wet lab methodology and small and/or large animal models are also required. Specific research background in the area of extremity trauma is highly desired. A track record of publication and excellent
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Participation Program at the U.S. Department of Defense . Qualifications The ideal candidate will have a bachelor's degree in engineering and should have a demonstrated track record of collecting and organizing
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such as turbulence modeling, interface tracking, and solution acceleration, which are relevant to both riverine and broader fluid dynamics applications. Collaboration with interdisciplinary teams will
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consumption, and trade data, wood uses in construction and other end uses, losses of wood from their uses, tracking of carbon contained in wood products throughout their lives and after their end of lives, and