Postdoctoral Fellow - Applied Machine Learning in Quantum Systems

Responsibilities

• Develop and deploy machine learning models for high-fidelity quantum operation inference and control prediction.
• Design and prototype in-the-loop control mechanisms that adapt sequences based on measurement outcomes and system state.
• Collaborate with physics, quantum error-correction, hardware, and control teams to validate new stack components using experimental data and system-level performance.

Requirements

• Ph.D. or equivalent experience in Physics, Computer Science, Electrical Engineering, or a related field, with a strong background in quantum computing or quantum physics.
• Experience working with quantum computing platforms (neutral atoms, trapped ions, superconducting qubits, or similar).
• Demonstrated experience working with Machine Learning for inference and hardware in loop.
• Strong programming skills in Python, C++, or similar languages.
• Strong analytical and problem-solving skills, with the ability to take technical ownership.
• Effective communication skills and the ability to collaborate across physics, engineering, and software teams.
• Proficiency with Git and modern collaborative development workflows.
• Track record of publications or significant technical contributions in relevant areas.

Qualifications

• Experience with quantum control stacks or real-time control systems.
• Familiarity with fault-tolerant protocols, logical qubit architectures, or quantum error correction frameworks.
• Experience with performance-sensitive, low-latency, or distributed systems.
• Awareness of advances in fault-tolerant quantum computing, in-the-loop control, and ML-driven control strategies.

Pay

The approximate base salary range for this position is $110,000-$120,000. We consistently monitor external market data and update base salary ranges accordingly.

Schedule

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Benefits

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Skills

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Application Instructions

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