Research Staff - HPC for Neuromorphic Systems

Oak Ridge National Laboratory; Oak Ridge (TN); On-site; USD 110,000 - 160,000; Full time

Benefits offered by this job

On-site fitness facilities

Relocation assistance

Educational assistance

Wellness programs

Job summary

Oak Ridge National Laboratory's Learning Systems Group seeks a Research Staff member to advance scalable algorithms, software, and AI-enabled methods for modeling, simulating, and co-designing spiking neural networks and neuromorphic systems on leadership-class and exascale computers.

You will lead distributed training and simulation workflows, develop AI-driven architecture search and co-design approaches, mentor students and early-career researchers, publish results, and help establish new

Qualifications

• PhD in a relevant field.
• Strong programming in Python and C/C++.
• Experience with parallel and distributed computing (MPI, distributed ML frameworks).
• Experience with GPU-accelerated computing (CUDA/ROCm).
• Experience with PyTorch or similar ML frameworks.
• Experience with collaborative software development (Git, containers, testing, documentation).
• Strong publication record in HPC/AI/scientific computing.
• Excellent written and oral communication skills.

Responsibilities

• Lead research in scalable algorithms and software for modeling, simulating, and optimizing SNNs and neuromorphic systems.
• Develop AI-driven architecture search and hardware-software co-design methods.
• Design and optimize distributed training and simulation workflows on leadership-class/exascale systems.
• Automate large-scale modeling and simulation campaigns and scientific workflows.
• Publish results and present at conferences and workshops.

Skills

Python

C/C++

MPI

Distributed computing

PyTorch

Git

Containers

Publications

Communication

Independent research

Multi-tasking

Education

PhD in Data Science and Engineering, CS, CE, EE, Math, or related field

Tools

CUDA

ROCm

GPU-accelerated platforms

HPC workload managers

Job description

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Research Staff - HPC for Neuromorphic Systems

As a research staff member, you will conduct and lead research at the intersection of high performance computing, artificial intelligence, and neuromorphic computing. You will develop scalable algorithms, software, and AI-enabled methods for modeling, simulating, and co-designing spiking neural networks and emerging computing systems. Drawing on expertise in distributed computing, machine learning, and architecture search, you will use leadership-class and exascale systems to evaluate and optimize new approaches. You will collaborate across disciplines, help establish new research directions, contribute to competitive proposals, mentor students and early-career researchers, and communicate results through peer-reviewed publications and presentations.

The Learning Systems Group at Oak Ridge National Laboratory advances the next generation of artificial intelligence by co-designing algorithms and computing systems to address complex challenges in science, energy, national security, and health. Our researchers develop scalable machine-learning models, deep-learning architectures, and data-driven methods for platforms ranging from edge sensors and neuromorphic systems to national supercomputers and quantum computers. Our work includes scientific knowledge discovery, autonomous and energy-efficient systems, threat intelligence and data fusion, agent-based modeling and digital twins, and predictive analysis for environmental and health applications. We are committed to recruiting and retaining highly motivated, creative research staff members and students who will help develop intelligent systems that learn, reason, and operate effectively across diverse computing environments.

Major Duties/Responsibilities:

• Conduct and lead original research in scalable algorithms and software for modeling, simulating, and optimizing spiking neural networks and neuromorphic systems.
• Develop AI-driven architecture search and hardware-software co-design methods for neuromorphic systems, including evolutionary and differentiable approaches where appropriate.
• Design, implement, optimize, and evaluate distributed training, inference, and simulation workflows on GPU-accelerated leadership-class and exascale computing systems.
• Develop methods for automating large-scale modeling and simulation campaigns, including agentic AI and scientific workflow orchestration where appropriate.
• Characterize performance, scalability, portability, reliability, data movement, and power consumption under load across heterogeneous computing platforms.
• Develop and maintain high-quality research software, including documentation, testing, reproducible workflows, and collaborative version control.
• Build strong collaborations within ORNL and across the high performance computing, artificial intelligence, and neuromorphic computing communities; contribute to proposals, project plans, milestones, and sponsor deliverables; and mentor students and early-career researchers.
• Publish research in peer-reviewed journals and conferences, author technical reports, and represent ORNL and the Learning Systems Group through presentations at conferences, workshops, and invited forums.
• Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success.

Basic Requirements:

• Ph.D. in Data Science and Engineering, Computer Science, Computer Engineering, Electrical Engineering, Mathematics, or a closely related field.
• Strong programming proficiency in Python and C or C++.
• Experience with parallel and distributed computing using technologies such as MPI, distributed machine-learning frameworks, and HPC workload managers.
• Experience using GPU-accelerated computing platforms and software stacks such as NVIDIA CUDA or AMD ROCm.
• Experience developing and scaling artificial intelligence, machine-learning, or scientific-computing workloads on multi-node systems.
• Experience with machine-learning frameworks such as PyTorch or equivalent tools.
• Experience with collaborative software-development practices and tools such as Git, containers, testing, and documentation.
• A strong record of peer-reviewed publications demonstrating original research in high-performance computing, artificial intelligence, machine learning, or computational science.
• Excellent written and oral communication skills.
• Ability to define and pursue research tasks independently while contributing effectively to multidisciplinary teams.
• Ability to manage multiple priorities, meet project deadlines, and adapt to changing research needs.

Preferred Qualifications:

• Experience with neural architecture search, evolutionary optimization, differentiable optimization, or hardware-software co-design.
• Knowledge of neuromorphic computing, spiking neural networks, and neuron models such as Integrate and Fire, Leaky Integrate and Fire, Izhikevich, or Hodgkin-Huxley.
• Experience developing HPC-scale simulators, benchmarking tools, or performance-evaluation frameworks for scientific or artificial intelligence applications.
• Experience with leadership-class or exascale systems and both NVIDIA and AMD GPU platforms.
• Experience with large-scale distributed training and inference using approaches such as data, model, pipeline, or fully sharded parallelism.
• Experience with agentic AI, large language models, retrieval-augmented generation, tool use, or AI-enabled scientific workflows.
• Experience with AI trustworthiness, security, evaluation, or adversarial testing in scientific-computing environments.
• Familiarity with neuromorphic hardware and other emerging computing technologies, including quantum, reversible, memristive, ferroelectric, spintronic, optoelectronic, or superconducting systems.

Special Requirements:

• For employment at Oak Ridge National Laboratory (ORNL), a Real ID compliant form of identification will be required. Additionally, ORNL is subject to Department of Energy (DOE) access restrictions. All employees must also be able to obtain and maintain a federal Personal Identity Verification (PIV) card as mandated by Homeland Security Presidential Directive 12 (HSPD-12) and Department of Energy (DOE) Order 473.1A, which requires a favorable post-employment background investigation.
• To obtain this credential, new employees must successfully complete and pass a Federal Tier 1 background check investigation. This investigation includes a declaration of illegal drug activities, including use, supply, possession, or manufacture within the last year. This includes marijuana and cannabis derivatives, which are still considered illegal under federal law, regardless of state laws.
• For foreign national candidates: If you have not resided in the U.S. for three consecutive years, you are not eligible for the PIV credential and instead will need to obtain a favorable Local Site Specific Only (LSSO) risk determination to maintain employment. Once you meet the three-year residency requirement, you will be required to obtain a PIV credential to maintain employment.

About ORNL:

As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an impressive 80-year legacy of addressing the nation’s most pressing challenges. Our team is made up of over 7,000 dedicated and innovative individuals! Our goal is to create an environment where a variety of perspectives and backgrounds are valued, ensuring ORNL is known as a top choice for employment. These principles are essential for supporting our broader mission to drive scientific breakthroughs and translate them into solutions for energy, environmental, and security challenges facing the nation.

ORNL offers competitive pay and benefits programs to attract and retain individuals who demonstrate exceptional work behaviors. The laboratory provides a range of employee benefits, including medical and retirement plans and flexible work hours, to support the well-being of you and your family. Employee amenities such as on-site fitness, banking, and cafeteria facilities are also available for added convenience.

Other benefits include the following:

• Prescription Drug Plan
• Dental Plan
• Vision Plan
• 401(k) Retirement Plan
• Contributory Pension Plan
• Life Insurance
• Disability Benefits
• Generous Vacation and Holidays
• Parental Leave
• Legal Insurance with Identity Theft Protection
• Employee Assistance Plan
• Flexible Spending Accounts
• Health Savings Accounts
• Wellness Programs
• Educational Assistance
• Relocation Assistance
• Employee Discounts

This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired.

ORNL is an equal opportunity employer. All qualified applicants, including individuals with disabilities and protected veterans, are encouraged to apply. UT-Battelle is an E-Verify employer.