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PhD Intern- Quantum Computing
Overview
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 Group at the Pacific Northwest National Laboratory (PNNL) is seeking highly motivated PhD Intern. We are looking for candidates with a strong background in any of the topics listed as follows:
- Quantum error correction, detection and mitigation.
- Deployment of quantum algorithms and protocols on near term hardware.
- Benchmarking, verification and validation toolkit development for quantum computing.
- Quantum resource estimation for NISQ and FTQC.
- Quantum algorithm optimization and transpilation for NISQ and FTQC.
- Quantum computing and high-performance computing’s intersection.
Responsibilities
- Develop novel methods: Contribute and research the state of the art in one or more of the aforementioned research topics, with close collaboration with staff scientists. This role centers around a subsection of the description list, and will rely on 1-3 topics mentioned above.
- Present research progress and work in weekly team meetings, communicating progress and results.
- Engage with interns, post-docs and staff scientists in helping develop the quantum ecosystem capabilities.
Minimum Qualifications
- Candidates must be currently enrolled/matriculated in a PhD program at an accredited college.
- Minimum GPA of 3.0 is required.
- Candidates should be pursuing a PhD in a relevant field such as quantum computing, physics, computer science, electrical engineering, applied mathematics, computational science, information science, chemistry (theoretical/physical), or data science.
- Research experience in one or more areas such as quantum error correction, NISQ algorithms, quantum benchmarking, quantum resource estimation, transpilation/optimization, or quantum–HPC integration.
- Experience working with quantum programming frameworks (examples: Qiskit, Cirq, Braket, PennyLane, or similar).
- Strong background in linear algebra, probability theory, optimization, or numerical methods.
- Experience with high‑performance computing environments or parallel programming.
- Demonstrated ability to conduct independent research and publish in peer‑reviewed venues.
- Familiarity with experimental or simulational quantum computing platforms.
- Strong programming skills in languages commonly used in scientific computing (such as Python, C , Julia, or similar).
- Ability to clearly present technical results and collaborate effectively with interdisciplinary teams.
- Prior internship or research lab experience (academic, national lab, or industry) is a plus.
