Post Doctorate Research Associate - Quantum Computing Systems Control

At PNNL, our core capabilities are divided among major departments that we refer to as Directorates within the Lab, focused on a specific area of scientific research or other function, with its own leadership team and dedicated budget.  

Our Science & Technology directorates include National Security, Earth and Biological Sciences, Physical and Computational Sciences, and Energy and Environment. In addition, we have an Environmental Molecular Sciences Laboratory, a Department of Energy, Office of Science user facility housed on the PNNL campus. 

The Physical and Computational Sciences Directorate's (PCSD’s) strengths in experimental, computational, and theoretical chemistry and materials science, together with our advanced computing, applied mathematics and data science capabilities, are central to the discovery mission we embrace at PNNL. But our most important resource is our people—experts across the range of scientific disciplines who team together to take on the biggest scientific challenges of our time. 

The Advanced Computing, Mathematics, and Data Division (ACMDD) focuses on basic and applied computing research encompassing artificial intelligence, applied mathematics, computing technologies, and data and computational engineering. Our scientists and engineers apply end-to-end co-design principles to advance future energy-efficient computing systems and design the next generation of algorithms to analyze, model, understand, and control the behavior of complex systems in science, energy, and national security. 

Responsibilities

The Future Computing Technology (FCT) Group at the Pacific Northwest National Laboratory (PNNL) seeks a postdoc researcher with a strong background in quantum computing, quantum physics, and control of quantum systems. The candidate will be expected to perform world-leading research focusing as part of the Pacific Northwest National Laboratory within its DOE and internal-funded quantum projects. The candidate will be expected to collaborate closely with laboratory personnel in computing and application domains, as well as researchers at collaborating national laboratories, universities, and industry on center activities focused on the development, application, compilation, and optimization of quantum algorithms and hardware.

Responsibilities and Accountabilities

• Building superconducting quantum testbed, calibration, and system maintenance
• Research and development on quantum control, superconducting chip fabrication, and quantum computing architecture
• Developing algorithms and tools for quantum system maintenance and integration with software stack
• Publish results in high-visibility, peer-reviewed venues (e.g., conferences and journals)
• Interact with internal and external research staff and domain scientists for collaboration purposes
• Participate in and potentially lead technical presentations of the work
• Participate in team meetings and potentially interact with funding sponsors

Qualifications

Minimum Qualifications:

• Candidates must have received a PhD within the past five years (60 months) or within the next 8 months from an accredited college or university

Preferred Qualifications:

• Advanced academic training in quantum physics, computer science, or a related discipline, with demonstrated research or applied experience at the intersection of these fields
• Hands-on expertise with quantum computing platforms and quantum optimal control, including familiarity with state-of-the-art control hardware such as OPX and QICK
• Proven experience developing and implementing quantum algorithms and contributing to compiler design for quantum applications
• Practical background in superconducting quantum device calibration, characterization, and maintenance, ensuring high-fidelity system performance
• Strong foundation in quantum noise models and/or the design and application of quantum error correction codes, with emphasis on reliability and scalability
• Experience in superconducting quantum device design and fabrication, including knowledge of materials, processes, and architecture optimization for next-generation hardware