132 big-data-and-machine-learning-phd Postdoctoral positions at The Ohio State University
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transplant models (heart, lung, liver, kidney), an operational small and large animal normothermic machine perfusion (NMP) model, and a precision-cut organ slice (Liver, Lung) platform. The Copper Laboratory
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describing research / outreach interests and fit with this position (2) curriculum vitae/resume (3) three references (contact only; no letters needed). Working Conditions: Sitting/staring at computer screens
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Respirator (PAPR) and other necessary PPE for up to 4 hours at a time. Ability to lift up to 40 lbs. Work with large animals or wildlife. Additional Information: Minimum Qualifications: PhD in Molecular
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. The successful candidate will play a key role in designing and implementing research at the intersection of sensor data collection, machine learning, remote sensing, and real-time agricultural decision-making
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methylation, chromatin accessibility), and clinical data. · Develop, apply, and benchmark machine learning and statistical models for subtype discovery, classification, and outcome prediction. · Contribute
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—integrating first-principles simulations with machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive
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Qualifications Experience with machine learning and deep learning Ability to analyze and interpret complex geophysical data and apply appropriate research methodologies Additional Information: The College of Arts
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-disciplinary team and lead ongoing watershed-scale modeling in U.S.EPA SWMM software. The candidate will be expected to analyze large rainfall, hydrologic, and water quality data sets, calibrate and validate
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interaction, electronic health record data, or large-scale biobank resources. Experience with high-performance computing, reproducible workflow development, scientific manuscript preparation, or collaborative
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analysis, machine learning, and data-driven modeling applied to large astronomical datasets and time-domain surveys is highly desirable. Although experience with microlensing and transit exoplanet searches