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Class Acad Prof and Admin Add to My Favorite Jobs Email this Job About the Job An NIH-funded postdoctoral position is available for a two-year program in the laboratory of Brendan Dougherty within
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, computer vision in the Division of Health Data Science (HDS) at the DOS. The position is an annually renewable professional academic appointment. Duties/Responsibilities: ● Risk predictive model for clinical
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, to evaluate the potential population count and distribution of beavers. - Use/develop appropriate computer modeling and remote sensing techniques to estimate vegetation greenness, burn severity, and total water
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an individual with strong theoretical and modeling skills, with a strong background in using satellite data to construct predictive models that allow the early detection of tree diseases from space. The research
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, and publication of major results from the experiment. They will also lead the development of predictive distribution models that incorporate data from the experiment. The project is funded by the USGS C
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faculty in developing theory and application tools for artificial intelligence (AI), and training efficient data analytics. 60% - Leading research in AI will include generative models, algorithms and
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flux measurements using biogeochemical modeling. They will be responsible for managing projects related to field instrumentation and ecosystem flux modeling. 20% designing and implementing trace gas flux
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will be working on MR spectroscopy in mouse models of creatine deficiency. Responsibilities: • Assist PI with animal preparation for scanning (15%) • Assist PI with data acquisition on the 16.4T animal
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appropriate models. The candidate is expected to follow best research practices including careful documentation of experiments and data, effective communication to ensure safety and reliability, and following
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the laboratory include the study of growth factor signaling in regulating growth and metastasis of breast cancer, use of animal models to study the effect of signaling inhibitors and natural killer cell based