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who qualify for the project. Work with physicians to obtain biopsy study samples. Data Collection, Management and Monitoring (40%) Conduct clinical assessments and documentation, biopsy acquisition, and
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basis of age (40 and over), color, disability, gender identity, genetic information, marital status, domestic partner status, military or veteran status, national origin/ancestry, race, religion, creed
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machine learning, data science or engineering, with two years of clinical experience. Successful candidates will have strong clinical background and an interest in population level data analytics
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for data-efficient exploration and optimization within the process parameter space as well as for adaptive, data-driven machine learning to map the electrolysis process to a digital twin. Data workflows and
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biological science and civilian mapping agency, USGS collects, monitors, analyzes, and provides science about natural resource conditions, issues, and problems. Research Project: The USGS Eastern Ecological
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status or military obligations, sexual orientation, gender identity or expression, genetic information, national origin, race (including hair texture and protected hairstyles such as natural hairstyles
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developing and applying advanced statistical models, machine learning, and deep learning approaches. As such, we seek applicants with strong quantitative backgrounds in remote sensing and time series analysis
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Remote Sensing; Machine Learning Models for Predicting Wildfire Spread; Wildfire Risk Assessment Through Multi-Modal Data Integration; Automated Vegetation and Fuel Load Mapping Using Computer Vision; AI
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N flux dynamics. NitroScope will make a combined use of different measurement techniques to reduce uncertainties and biases in N flux monitoring, including proximal sensing, remote sensing, Eddy