Postdoctoral Research Associate - Programming Systems for Extreme Heterogeneity

Oak Ridge National Laboratory • Oak Ridge, TN

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About The Position

The Computer Science and Mathematics Division at Oak Ridge National Laboratory is seeking a postdoctoral researcher to advance programming systems for extreme-scale heterogeneous computing. The work spans compiler infrastructure, formal verification, runtime systems, and hardware/software co-design, with potential exploration of quantum and analog computational models.

The researcher will investigate how compilers, runtimes, and AI-driven agents can co-optimize complex architectures across CPUs, GPUs, custom accelerators, and emerging quantum and analog devices. The position contributes to autonomous system engineering, where AI agents reason about algorithmic intent, architectural constraints, and performance models to support system co-design across the computing stack.

The appointment may last up to 24 months, with potential extension. Initial appointments and extensions depend on performance and funding availability. Applicants must have completed all Ph.D. requirements before starting and must have received their Ph.D. no more than five years before applying. Three letters of reference are required. Employment requires appropriate identification and successful completion of the applicable federal background or site-specific credentialing process.

Requirements

• Ph.D. in Computer Science, Computer Engineering, or a closely related field.
• Strong experience with LLVM, such as writing passes or performing IR transformations, and HPC programming in C++, MPI, OpenMP, CUDA, or related models.
• Solid understanding of distributed-memory systems and many-core architectures.
• Must have received the Ph.D. no more than five years before the application date and complete all degree requirements before the appointment begins.
• Submit three letters of reference.
• Meet applicable employment credentialing requirements, including a favorable federal background investigation or, for certain foreign national candidates, a favorable Local Site Specific Only risk determination.

Nice To Haves

• Experience with automated reasoning or verification tools such as Z3, TLA+, or LEAN.
• Background in linear algebra, graph algorithms, and related areas relevant to dataflow and tensor optimization.
• Familiarity with architectural simulators such as Gem5 or SST, or with hardware description languages (HDLs).
• Exposure to quantum programming models such as QIR, Q#, Qiskit, or Cirq, or to analog or neuromorphic systems; interest in integrating these paradigms into unified compilation and optimization strategies.
• Experience with Julia, Rust, Python, Kokkos, OpenSHMEM, or similar emerging ecosystem tools.

Responsibilities

• Advance LLVM to support closed-loop optimization environments in which AI agents analyze, transform, and validate intermediate representation (IR) using performance-driven reasoning.
• Investigate how AI agents can guide architectural design decisions for massively parallel heterogeneous systems, including possible integration of quantum or analog co-processors.
• Profile, model, and optimize distributed codes using MPI, OpenMP, CUDA, ROCm, and related high-performance computing technologies, while accounting for real hardware constraints.
• Explore how compilers and runtimes could unify classical, quantum, and analog execution models under a shared optimization framework; this is an optional emphasis.

Benefits

• Medical plans
• Retirement plans
• Flexible work hours
• On-site fitness, banking, and cafeteria facilities
• Prescription Drug Plan
• Dental Plan
• Vision Plan
• 401(k) Retirement Plan
• Contributory Pension Plan
• Life Insurance
• Disability Benefits
• 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

What This Job Offers

• Job Type: Full-time
• Career Level: Intern
• Education Level: Ph.D. or professional degree