Postdoctoral Scholar in AI and Quantum Computing

Position Description

Postdoctoral Scholar in AI and Quantum Computing
University of California, Los Angeles — Computer Science
Location: Los Angeles, California
Faculty supervisor / research group: Professors Jason Cong and Yizhou Sun
Expected start date: Winter Quarter of 2027
Appointment duration: 2-3 years
Salary: $75K maybe adjusted based on experience

The UCLA research team invites applications for a postdoctoral scholar to lead and conduct research in AI and quantum computing, focusing on quantum error correction, algorithms under realistic noise, and quantum-assisted scientific simulation.

We welcome candidates with strong research experience in AI and machine learning, quantum computing, or both. AI researchers interested in quantum applications and quantum researchers interested in developing AI methods are encouraged to apply. Prior expertise across every topic below is not expected.

The selected researcher will work closely with UCLA faculty, students, and collaborators to shape research directions, develop algorithms and software, and carry projects through rigorous evaluation and publication. The role combines independent scientific judgment with substantial hands-on implementation and technical ownership of research directions matched to the candidate’s expertise.

Research responsibilities. Priorities will be established with the faculty supervisor based on the candidate’s strengths and project needs.

• Efficient quantum error correction and noise management. Design and evaluate AI methods for quantum error-correction decoding, with particular interest in quantum low-density parity-check (qLDPC) codes. Investigate efficient sequence models and techniques such as quantization and pruning to meet decoding latency and computational resource targets. Develop methods for noise prediction, fidelity estimation, and quantum-state optimization using graph models, transformers, or generative models. Evaluate error mitigation approaches, including zero-noise extrapolation, alongside appropriate error-correction strategies.
• Quantum algorithms under realistic errors. Develop and analyze algorithms for optimization, sampling, and many-body physics on early fault-tolerant quantum systems. Explore algorithmic primitives involving mid-circuit measurement, classical feedforward, and engineered dissipation. Characterize how different error mechanisms affect algorithm performance, and jointly design algorithms, compilation, and error-control strategies to reduce the cost of reliable computation. Assess potential computational advantages through careful comparison with relevant classical and quantum baselines.
• Quantum-assisted machine learning for scientific simulation. Develop hybrid quantum and classical learning workflows for fermionic systems, chemistry, and molecular dynamics. Build training-data pipelines, train models with uncertainty quantification, and investigate adaptive workflows that request quantum calculations when classical model predictions are uncertain. Evaluate prediction accuracy, uncertainty calibration, inference speed, and the computational cost and benefit of quantum queries.

Research leadership and implementation. The postdoc will:

• Translate research objectives into concrete questions, technical plans, milestones, and measurable outcomes in collaboration with faculty.
• Personally implement algorithms, train and evaluate models, run simulations, and maintain documented research software.
• Establish reproducible benchmarks, strong baselines, and rigorous evaluation procedures.
• Mentor students in problem formulation, experimental design, and interpretation of results. Coordinate with collaborators and lead contributions to papers, presentations, and project reports.

Required qualifications. Candidates should have:

• A Ph.D. in computer science, electrical or computer engineering, physics, applied mathematics, or a related field by the start date.
• A strong research background in AI/ML (such as deep learning, optimization, or scientific machine learning) or quantum computing (such as algorithms, error correction, or simulation), demonstrated through publications, research software, or comparable technical achievements.
• Strong programming and mathematical skills, sound experimental judgment, and a commitment to reproducible research.
• Ability to lead a research project from problem formulation through implementation and evaluation.
• Clear communication, collaborative skills, and motivation to develop complementary AI or quantum expertise.

Preferred experience. Experience in one or more of the following is helpful; expertise in all areas is not expected:

• Graph neural networks, graph transformers, generative models, efficient attention or recurrent sequence models, and model compression.
• Quantum error correction, qLDPC codes, decoding, fault tolerance, noise modeling, or error mitigation.
• Quantum optimization, sampling, non-unitary algorithms, or many-body and fermionic simulation.
• Scientific machine learning, molecular modeling, uncertainty quantification, or adaptive data acquisition.
• GPU computing, performance optimization, reusable research software, or student mentoring.

Expected contributions. Develop new research methods supported by rigorous analysis or experiments, documented software, and reproducible benchmarks. Results should establish tradeoffs among accuracy, runtime, decoding latency, quantum resources, and scientific usefulness, and lead to scholarly publications.

Application materials. Please submit the following to [application email or portal]:

• A CV with publications and relevant software contributions.
• A cover letter describing your research background, interest in the position, and experience leading and implementing research.
• A brief research statement explaining which project directions best match your expertise and identifying potential research ideas.
• Up to three representative papers, software projects, or other research outputs.
• Contact information for three references.