Uncertainty Analysis for Machine Learning and Optimization Applications
This project will develop uncertainty analysis techniques for machine learning and optimization in areas like medicine and biomanufacturing to improve model reliability.
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NRC Research Associateship Programs has archived this opportunity.
Funder: NRC Research Associateship Programs
Due Dates: February 1, 2025 | May 1, 2025 | August 1, 2025 | November 1, 2025
Funding Amounts: Stipend approximately $82,764 per year plus $3,000 travel allowance; typical appointment duration 2 years.
Summary: Supports postdoctoral research to develop uncertainty analysis techniques for machine learning and optimization in fields such as precision medicine and biomanufacturing to improve model reliability.
Key Information: Open to U.S. citizens with a doctoral degree earned within the last 5 years; research conducted onsite at NIST in Gaithersburg, MD; requires prior contact with research adviser.
Description
This fellowship opportunity under the NRC Research Associateship Programs focuses on advancing computational techniques for uncertainty analysis in machine learning and optimization. The goal is to improve the robustness and reliability of predictive models by developing and applying uncertainty estimation methods ranging from non-parametric approaches like bootstrapping to fully Bayesian analyses.
Research areas include precision medicine, biomanufacturing, omics research, and kinetic model development. The project aims to enhance model predictions by quantifying uncertainty, thereby supporting more reliable decision-making in scientific and engineering applications.
The research will be conducted at the National Institute of Standards and Technology (NIST), specifically within the Material Measurement Laboratory, Chemical Sciences Division, located in Gaithersburg, Maryland.
