Postdoctoral Appointee - Computational Neuroscience; Onsite

About Sandia

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 401(k), learning opportunities, relocation assistance and amenities aimed at creating a solid work/life balance*

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* These benefits vary by job classification.

Are you passionate about working in collaboration with a hardworking team of neuroscientists, computer scientists, mathematicians, and engineers? Our team is seeking a computational neuroscience postdoctoral appointee to conduct research at the interface of neuroscience, artificial intelligence, and implementing neural‑inspired algorithms on conventional and specialized hardware.

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

• Draw inspiration from neuroscience to develop novel algorithms to tackle real‑world problems.
• Develop neurobiological models at differing levels of biological complexity, ranging from neural network theory to neural circuit models constrained by current understanding of biophysical mechanisms underlying sensory processing and learning.
• Work with computer scientists, mathematicians, and engineers to abstract neural computations such as dendritic computation, short‑term plasticity, and microcircuit computations for incorporation into artificial intelligence algorithms and human‑made computing systems.
• Develop techniques for mapping novel brain‑inspired and AI models onto existing and emerging neuromorphic hardware, including analyzing, benchmarking, and modeling neuromorphic architectures.
• Publish research results in journals and professional conference venues.

* Due to the nature of the work the selected applicant must be able to work onsite in Albuquerque, New Mexico. Relocation will be provided for those that qualify.

• Possess, or be pursuing, a PhD in theoretical neuroscience, computer science, mathematics, physics or a relevant field;
  PhD must be conferred within five years prior to employment.
• Experience with theoretical or practical aspects of either neural algorithms, artificial intelligence models, or neuromorphic hardware.
• Experience in a common programming language such as Python or C++.
• Able to obtain and maintain a DOE Q clearance.

• Interest in developing neural‑inspired (e.g., spiking neural network, population codes) and cutting‑edge artificial intelligence algorithms (e.g. transformer models, RNNs, diffusion models) or in the deployment and application of such algorithms.
• Experience with specialized computational architectures such as GPUs, FPGAs, neuromorphic processors.
• Experience with neural network modeling languages (PyTorch, Tensor Flow, Keras, etc.) or neural modeling languages (Brian, NEST, etc.).
• Prior peer‑reviewed publications in top‑tier journals are highly desirable.
• Desire to work as part of a collaborative, multi‑disciplinary team.

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 heterogeneous architectures based on integrated commodity components, special‑purpose accelerators, neural architectures and algorithms.

This posting will be open for application…