Research Aide-Crabtree - MCS - Le, Tristain - 3.11.26.
About The Position
This project will design a stochastic derivative-free trust-region algorithm for Gaussian-noisy black-box objectives, using statistical inference to adapt sample sizes and make noise-aware acceptance decisions. To scale to high dimensions, the approach will build and optimize random-subspace models, and will explore a two-model mechanism in which the algorithm simultaneously leverages trust-region models built from the lower and upper confidence bounds of the unknown true objective values. Deliverables include a well-tested Python package with documentation and reproducible experiments on standard benchmarks and DOE-relevant problems. Education and Experience Requirements The entirety of the appointment must be conducted within the United States. Applicants must be: o Currently enrolled in undergraduate or graduate studies at an accredited institution. o Graduated from an accredited institution within the past 3 months; or o Actively enrolled in a graduate program at an accredited institution. Must be 18 years or older at the time the appointment begins. Must possess a cumulative GPA of 3.0 on a 4.0 scale. If accepting an offer, candidates may be required to complete pre-employment drug testing based on appointment length. All students remain subject to applicable drug testing policies. Must complete a satisfactory background check.
Requirements
- The entirety of the appointment must be conducted within the United States.
- Currently enrolled in undergraduate or graduate studies at an accredited institution.
- Graduated from an accredited institution within the past 3 months; or
- Actively enrolled in a graduate program at an accredited institution.
- Must be 18 years or older at the time the appointment begins.
- Must possess a cumulative GPA of 3.0 on a 4.0 scale.
- If accepting an offer, candidates may be required to complete pre-employment drug testing based on appointment length. All students remain subject to applicable drug testing policies.
- Must complete a satisfactory background check.
Benefits
- comprehensive benefits are part of the total rewards package.
- Click here to view Argonne employee benefits!
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What This Job Offers
Job Type
Part-time
Career Level
Entry Level
Education Level
No Education Listed
Number of Employees
1,001-5,000 employees
