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, wearables, sleep and immune function, human microbiome analysis, metabolomic analysis, cancer detection, and related analysis methods relating to the health and human sciences Multidisciplinary modeling
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efficiency and reduce costs. In this project, the participant will collect liquid samples of condensed exhaust gas from CO2 capture tests and utilize methods to determine the identity and concentrations
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into the use of molecular, immunological, synthetic biology, and mass spectrometry methods to develop capabilities for the detection of and protection from current and emerging biological threats. The internship
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within the past three years. Preferred skills: Soil sampling methods Stable isotope mass spectrometry Elemental analyzers Carbon fractionation methodology Collecting and analyzing soil gas samples Nitrogen
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skills by engaging in the full cycle of research activities, including developing research questions and methods, reviewing literature, analyzing data, writing publications for peer review, and presenting
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-resistant weeds, supporting the development of sustainable agricultural practices. The successful candidate is expected to (1) help develop novel methods that incorporate advanced technologies for detecting
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applying models of insect movement, and develop predictive models. This research aims to collect movement data and produce models to enhance current surveillance, control, and eradication methods, such as
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machine learning methods for disease quantification. Through the course of the project, they will gain in-depth knowledge of issues and the latest research at the junction of plant pathology and plant
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, and testing of new scientific methods. More specifically, you will focus on investigating satellite imagery data sources and developing methods for deriving water depths, product bathymetry products
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collaborating with research scientists and technicians, the intern will also learn methods of experimental design, field trials, trait assessment, and data collection. Opportunities to analyze and summarize data