459 machine-learning-"https:"-"https:"-"https:" Fellowship positions in United States
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managing diabetes using computer simulation models. Learning Objectives: You will learn: How to synthesize and translate empirical evidence on cost-effectiveness of interventions for the prevention and
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and capability gaps. Learning Objectives: Through this opportunity, you will gain knowledge and practical experience in biosurveillance, emerging biological threats, public health preparedness, program
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machine learning methods, including cluster analysis and predictive modeling, to identify distinct phenotypes of diabetes and characterize factors associated with disease onset, progression, complications
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at the intersection of marine ecology, ocean technology, machine learning, and high-throughput biological imaging. This position offers a rare opportunity to help pioneer the use of advanced shadowgraph imaging systems
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Oden Institute for Computational Engineering and Sciences | Austin, Texas | United States | 2 months ago
genetic data Multimodal machine learning for biological discovery Translational genomics and risk modeling The fellow will work in an environment that emphasizes methodological innovation, statistical rigor
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to multidisciplinary research aimed at advancing military medicine. What will I be doing? This opportunity offers a hands-on learning experience within a collaborative research environment focused on combat casualty
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technology or Artificial Intelligence (AI) tools to solve business or administrative problems, demonstrated knowledge with Machine Learning (ML) and AI tools. Strong integration experience in enterprise
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often and stress level is moderate to high. Noise level is quiet to moderate. Physical Activities Ability to work in front of a computer for extended periods of time. Occasionally required to move about
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the shallow subsurface (<10 meters depth). Experience with soil moisture/salinity and sapflow sensors. Experience using neural networks and machine learning tools. Stipend $70,000.00 – $80,000.00 Yearly Point
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machine learning. The successful candidate will develop and apply methods that integrate multimodal molecular and clinical data (genomic, epigenomic, transcriptomic) across serial patient timepoints