Professional Research Assistant

Job Summary

This position will involve developing and analyzing perturbative algorithms tailored for learning in hardware neural networks. This includes designing methods where small, controlled perturbations to network parameters facilitate learning, as opposed to traditional backpropagation-based optimization. The researcher will explore model-free approaches to perturbation-based learning and assess their effectiveness by implementing them on hardware implementations. A key focus will be on ensuring that these algorithms are robust to noise, compatible with constraints imposed by neuromorphic and analog computing architectures, and scalable to large networks. The researcher will be responsible for implementing and benchmarking perturbative learning algorithms on specialized hardware platforms such as custom analog circuits. This will involve low-level programming, hardware-aware optimization, and the integration of software-hardware co-design principles to maximize learning efficiency. This position is part of the National Institute of Standards and Technology's (NIST) Professional Research Experience Program (PREP). NIST recognizes that its research staff may want to collaborate with researchers at academic institutions on specific projects of mutual interest and, therefore, requires those institutions to be recipients of a PREP award. The PREP program involves staff from a wide range of backgrounds conducting scientific research across various fields. Individuals in this position will perform technical work supporting the collaboration's scientific research.

Who We Are

PREP is a special partnership between the National Institute of Standards and Technology (NIST) and the University of Colorado Boulder. PREP provides research opportunities to CU undergraduate and graduate students, as well as researchers with a Bachelor's, Master's, or PhD in NIST labs to gain research experience alongside NIST scientists.

The particular group that needs a researcher is the Faint Photonics Group at NIST.

What Your Key Responsibilities Will Be

• Conduct research on perturbative learning algorithms for hardware-implemented neural networks.
• Develop novel techniques for efficient weight adaptation, noise-tolerant learning, and hardware-friendly optimization.
• Analyze the theoretical foundations of perturbative methods in the context of learning dynamics and energy efficiency.
• Implement and benchmark algorithms on hardware platforms such as analog computing devices.

What We Can Offer

The salary range for this full-time position is $54,000 - $78,000 annually.

What We Require

• Bachelor's degree in a STEM related field.

What We Would Like You to Have

• Familiarity with coding in Python.
• Ability to process large amounts of data.
• Familiarity with operation of SLURM and cluster-based computation.
• Ability to develop prototypes of tools needed to analyze data.
• Strong oral and written communication skills.

Special Instructions

To apply, please submit the following materials:

1. Resume or CV
2. Cover letter addressed to the Search Committee briefly describing your qualifications, professional goals, and specific interest in this position.

If you are selected as the finalist, your degree will be verified by the CU Boulder Campus Human Resources Department using an approved online vendor. If your degree was obtained outside of the United States, please submit a translated version as an optional attachment.

During the application process you will need to enter contact information for a reference. We will request the letter of recommendation immediately following your application.

Review of applications will begin immediately and remain open until April 13, 2026.

Note: Application materials will not be accepted via email. For consideration, applications must be submitted through CU Boulder Jobs.