Postdoctoral Appointee: Neural Networks, Onsite

Job

Location:

Albuquerque, NM

Full/Part Time:
Full‑Time

Regular/Temporary:
Temporary

Sandia National Laboratories is the nation's premier science and engineering lab for national security and technology innovation, with teams of specialists focused on cutting‑edge work in a broad array of areas. Some of the main reasons we love our jobs:

• Challenging work with amazing impact that contributes to security, peace, and freedom worldwide
• Extraordinary co‑workers
• Some of the best tools, equipment, and research facilities in the world
• Career advancement and enrichment opportunities
• Flexible work arrangements for many positions include 9/80 (work 80 hours every two weeks, with every other Friday off) and 4/10 (work 4 ten‑hour days each week) compressed workweeks, part‑time work, and telecommuting (a mix of onsite work and working from home)
• Generous vacation, strong medical and other benefits, competitive 401k, learning opportunities, relocation assistance and amenities aimed at creating a solid work/life balance

World‑changing technologies. Life‑changing careers. Learn more about Sandia at: http://(Use the "Apply for this Job" box below)..gov

These benefits vary by job classification.

We are seeking motivated postdoctoral candidates to advance the theory of brain‑inspired algorithms and apply them to complex, real‑world problems. The successful candidate will join an interdisciplinary team of computer scientists, mathematicians, engineers, and neuroscientists pursuing advanced research and development in neuromorphic computing, artificial intelligence, and spiking neural networks across a range of applications.

A strong background in theory (e.g., mathematics, theoretical neuroscience, computer science) is a necessity. Experience with artificial intelligence models or neural‑inspired computing is strongly preferred.

On any given day, you may be called on to:

• Develop and extend neural‑inspired artificial intelligence algorithms;
• Research theoretical principles and foundations necessary for model interpretability and introspection;
• Program and test algorithms on neuromorphic and AI accelerator hardware;
• Apply existing state‑of‑the‑art methods and algorithms to domain data;
• Engage with the community for conferences, workshops, proposals, and outreach;
• Develop and support open‑source research software packages;
• Publish research results in high‑quality journals and competitive conference venues. Due to the nature of the work, the selected applicant will be required to work onsite. Relocation provided for those who qualify.

• PhD in computer science, mathematics, theoretical neuroscience, physics, or a relevant field conferred within 5 years prior to start date.
• Experience with theoretical or practical aspects of either neuromorphic algorithms or artificial intelligence.
• Experience in a common programming language such as Python or C++.
• Ability to obtain and maintain a DOE Q clearance.

• Interest in developing neural‑inspired and cutting‑edge artificial intelligence algorithms (e.g., spiking neural networks, Bayesian neural networks, RNNs, LLMs) or in the deployment of such algorithms.
• Experience with specialized computational architectures such as GPUs, FPGAs, neuromorphic processors, or machine learning accelerators.
• Experience with neural network modeling languages (PyTorch,Tensorflow, Keras, etc.) or neural modeling languages.
• Prior peer‑reviewed research publications.
• Demonstrated ability to work with others and contributed in a team environment.
• Desire to work as part of a collaborative, multi‑disciplinary team.
• Willingness to be flexible in assignments and develop expertise in different areas of neuromorphic computing.

The department of Cognitive & Emerging Computing (Org. 01421) pursues foundational research and development of:

• emerging general‑purpose energy‑efficient and beyond‑Moore computing paradigms;
• cognitive, brain‑inspired and neuromorphic computing, human‑machine interface technologies;
• advanced heterogenous architectures based on integrated commodity components, special purpose accelerators, neural architectures and algorithms.

This posting will be…