PhD Intern- Quantum Computing at Pacific Northwest National Laboratory

Wayne State University; New York (NY); On-site; USD 20,000 - 30,000; Full time

Job summary

A leading research institution in New York City is seeking highly motivated PhD interns for 2026 to engage in cutting-edge quantum computing research. Interns will work closely with staff scientists, presenting their research progress and contributing to the development of quantum ecosystem capabilities. Successful candidates will demonstrate expertise in quantum error correction and machine learning techniques. This three-month internship offers opportunities to collaborate on impactful projects within a world-class team.

Qualifications

• Candidates must be currently enrolled/matriculated in a PhD program at an accredited college.
• Minimum GPA of 3.0 is required.
• Demonstrated expertise in computational reliability techniques.

Responsibilities

• Contribute and research various topics with close collaboration with staff scientists.
• Present research progress in weekly team meetings.
• Engage with team members to develop quantum ecosystem capabilities.

Skills

Quantum error correction

Machine learning techniques

Quantum algorithms and protocols

Education

PhD program enrollment

Tools

Quantum hardware platforms

Job description

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 interns for 2026.

• Contribute and research a variety of topics with close collaboration with staff scientists
• 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

The internship is three months long, subject to extension based on performance and project needs. Interns can engage in cutting-edge research and contribute to impactful projects alongside our world‑class team. This position will collaborate with a team in New York City.

Qualifications

Minimum Qualifications

• Candidates must be currently enrolled/matriculated in a PhD program at an accredited college.
• Minimum GPA of 3.0 is required.

Preferred Qualifications

• Demonstrated expertise in quantum error correction, detection, and mitigation strategies to enhance computational reliability.
• Experience with deploying quantum algorithms and protocols on near‑term quantum hardware platforms.
• Proficiency in machine learning techniques for quantum computing, including hybrid quantum‑classical approaches.
• Research or applied experience in machine learning–based simulation of quantum systems.
• Knowledge of emerging quantum technologies, such as neutral atom systems, erasure qubits, or related architectures.
• Background in developing benchmarking, verification, and validation toolkits for assessing quantum hardware and algorithms.
• Experience with quantum resource estimation for both NISQ (Noisy Intermediate-Scale Quantum) and fault‑tolerant quantum computing (FTQC) regimes.
• Proven ability in quantum algorithm development and performance optimization for real‑world problem domains.
• Familiarity with quantum circuit optimization and transpilation techniques applicable to NISQ and FTQC environments.