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Sandia National Laboratories · 7 hours ago

Postdoctoral Appointee - Neuromorphic Codesign (Onsite)

Albuquerque, NM

Full-time

Onsite

New Grad, Entry Level

Sandia National Laboratories is the nation’s premier science and engineering lab for national security and technology innovation. The role involves conducting research on deploying artificial intelligence and computational neuroscience algorithms onto neuromorphic computing hardware within a highly interdisciplinary team.

GovernmentInformation TechnologyNational Security

Growth Opportunities

No H1B

Security Clearance Required

U.S. Citizen Only

Responsibilities

Co-design novel brain-inspired and machine learning algorithms and novel neuromorphic hardware. This includes analyzing, bench-marking, and modeling neuromorphic architectures

Find opportunities to improve future neuromorphic architectures targeting new application domains

Use neuromorphic sensors (such as event cameras) and neuromorphic processing to target edge computing applications like space and remote systems, autonomous vehicles, and cybersecurity

Publish research results in high-quality journals and professional conference venues

Qualification

PhD in relevant fieldAlgorithm developmentNeuromorphic hardware experienceProgramming in PythonProgramming in C++Programming in MATLABNeural network modelingSoft skills

Required

Possess, or are pursuing, a PhD in electrical engineering, computer engineering, computer science, or related field; PhD must be conferred within five years prior to employment

Experience in developing and optimizing algorithms to specialized architectures such as neuromorphic hardware, FPGAs, GPUs, or machine learning accelerators

Experience in a common programming language such as Python, C++, or MATLAB

Able to acquire and maintain a DOE Q-level security clearance

Preferred

Interest in neural-inspired and artificial intelligence algorithms (e.g. deep neural networks, spiking neural networks, recurrent neural networks)

Interest in resource (e.g. SWaP or Data) constrained AI and deep learning methods and techniques

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 (e.g. PyNN, NEST, NEURON)

Experience in algorithm-hardware co-design

Prior research activity, publications in computing or electrical engineering conferences is highly desirable (a list of publications should be included in your CV)