Uncertainty Quantification for Machine Learning
at ORAU Workforce Solutions

Date Posted: 2/1/2019

Opportunity Description

Title of Research Opportunity: Uncertainty Quantification for Machine Learning There has been a growing interest to deploy advanced machine learning model in intelligent systems. While there has been a lot of work on producing accurate output, there is less attention on the uncertainty of the output. The aim of this research is to develop theoretical foundation and computational framework to estimate the uncertainty in large-scale learning models. These uncertainty measure will be utilized to i) Explore trade-offs with computational complexity, learning time, and accuracy for various intelligent system application. ii) Provide feedback to higher level artificial reasoning intelligent systems assisting missions. iii) Enable optimal risk-aware learning. Research opportunities exist in the following areas: • Uncertainty quantification for DNN • Gaussian Processes for classification/regression in Machine Learning • Bayesian Sampling in Neural Network • Stochastic Optimization Keywords: Machine Learning, Statistical Learning, Supervised Learning, Uncertainty Quantification, Gaussian Processes, Stochastic Optimization Click here for more information

Opportunity Snapshot

About Us

Oak Ridge Associated Universities (ORAU) administers Science, Technology, Engineering and Mathematics (STEM) research participation programs for civilians such as:

The U.S. Army Research Laboratory (ARL) Research Associateship Program (RAP) allow Postdoctoral Fellows, Journeyman Fellows (undergraduate and graduates students and recent graduates), Senior Researchers, and Summer Faculty engage in research initiatives of their own choice, that are compatible with the interests of the government and will potentially contribute to the general effort of the ARL. Scientists and engineers at ARL help shape and execute the Army's program for meeting the challenge of developing technologies that will support Army forces in meeting future operational needs.

Research opportunities include, but are not limited to the following disciplines: Aerospace Engineering, Anthropology, Archeology, Biology, Biochemistry, Biological Engineering, Biomechanical Engineering, Biomedical Engineering, Chemical Engineering, Chemistry, Computer Science, Computer Engineering, Electrical Engineering, Environmental Health Risk Assessment, Environmental Science, Entomology, Epidemiology, Ergonomics, Geology, Health Education Mechanical Engineering, Materials Science, Mathematics, Nanotechnology, Photonics, Physics, Public Health Economics, Public Health Policy, and more.

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