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Field
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focuses on developing cutting-edge statistical/machine learning methods for fitting complex network models to partially observed hospital infection data, leveraging patient movement data. This research will
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the soil-water-plant-air continuum using process-based models. You will learn how to take proper soil, plant and air samples that influence carbon and nitrogen dynamics and learns how soil and plant
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uncertainty. Utilize machine-learning and data-mining approaches to recommend bioengineering interventions. Develop new machine-learning algorithms. Integrate machine learning techniques with mechanistic
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or connectivity analysis; machine-learning or deep-learning methods for geospatial analyses; ecological or remote-sensing fieldwork, particularly in alpine environments; Google Earth Engine, geodatabases or cloud
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the application of machine learning/artificial intelligence (ML/AI) in environmental health. This project aligns with ATSDR's current strategic initiatives and will provide you with opportunities
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research on novel brain machine interface technology. The role involves comprehensive testing of chronically implanted carbon fiber electrode arrays in neural recording and stimulation applications. Specific
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submitted. If email is not received, check the junk folder before contacting ORISE to confirm submission. To apply and learn more about EIF and host institutions – such as eligibility, requirements, and
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Science at Johns Hopkins University seeks an outstanding postdoctoral fellow to lead scientific efforts on machine learning applications to signed languages to begin by Fall 2026. The fellow will work
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preferred skill, including GIS, spatial suitability modeling, multi-criteria decision methods, and machine-learning-based feature importance. Exposure to resource questions relevant to large-load development
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experiences on computer vision • Strong programming skills in using Deep Learning tools like PyTorch and GPU clusters. • Good written and verbal communications. • Open to Fixed Term Contract