Postdoctoral Research Associate - AI Quantum Computing

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As a postdoctoral researcher, you will help solve some of the most challenging problems the world faces. You will perform impactful research on a range of significant problems, and you will apply your work in multidisciplinary domains alongside globally recognized experts. You will bring creative thinking, teamwork, and hardware-software co-design skills to bear as you develop new methods to address scientific and engineering problems, collaborate with leaders in your field and across the laboratory, and disseminate innovative results through publications and presentations.

The Learning Systems Group at Oak Ridge National Laboratory (ORNL) seeks a highly motivated Postdoctoral Research Associate with strong expertise in AI for quantum computing to lead research at the frontier of advanced machine learning and quantum information science. The successful candidate will leverage deep neural architectures, neural decoders for quantum error correction (QEC), error mitigation, and noise characterization to design and evaluate novel AI‑assisted quantum workflows, agentic systems, and physics‑informed ML algorithms for quantum computation.

The candidate will collaborate closely with machine learning researchers, software engineers, and quantum information scientists across the Quantum Science Center (QSC). Research directions center on AI‑driven quantum workflows, including deep neural decoders for real‑time QEC, AI‑assisted noise learning, automated quantum circuit synthesis and optimization, and multi‑agent AI systems for executing, decoding, and benchmarking experiments on state‑of‑the‑art quantum hardware and HPC systems.

The position offers access to leading quantum hardware and HPC facilities, opportunities to publish in top AI and quantum venues, and the chance to contribute to open‑source quantum AI software.

• Conduct original research in AI for quantum computing, developing advanced machine learning models, neural decoders for quantum error correction, AI‑driven error mitigation, and noise characterization algorithms.
• Design, build, and evaluate AI‑assisted quantum workflows, combining deep learning architectures and multi‑agent AI systems to automate circuit generation, QEC decoding, and experiment analysis across quantum software frameworks (such as Qiskit, Penny Lane, CUDA‑Q, Stim) and quantum hardware backends.
• Develop benchmarks and validation methods that test whether AI‑generated quantum code, circuits, and analyses are physically and computationally correct.
• Implement and benchmark quantum algorithms and error‑handling protocols on quantum hardware from multiple providers and modalities, and on GPU/HPC resources, to ground AI systems in realistic device behavior.
• Publish in leading peer‑reviewed venues, release open‑source software, contribute to proposals, mentor students, and collaborate across ORNL and with academic, industry, and national laboratory partners, following ORNL policies for sensitive information and cybersecurity.

• Conduct original research in AI for quantum computing, developing advanced machine learning models, neural decoders for quantum error correction, AI‑driven error mitigation, and noise characterization algorithms.
• Design, build, and evaluate AI‑assisted quantum workflows, combining deep learning architectures and multi‑agent AI systems to automate circuit generation, QEC decoding, and experiment analysis across quantum software frameworks (such as Qiskit, Penny Lane, CUDA‑Q, Stim) and quantum hardware backends.
• Develop benchmarks and validation methods that test whether AI‑generated quantum code,…