Research Staff - HPC for Neuromorphic Systems

Oak Ridge National Laboratory•Oak Ridge, TN

6d•Onsite

About The Position

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.

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.

Nice To Haves

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.

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.