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
• Strong background in quantum computing or quantum physics
• Experience working with quantum computing platforms such as neutral atoms, trapped ions, or superconducting qubits
• Demonstrated experience working with Machine Learning for inference and hardware in loop
• Strong programming skills in Python or C++
• Proficiency with Git and modern collaborative development workflows
• Track record of publications or significant technical contributions

Preferred 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
• Knowledge of advances in fault-tolerant quantum computing and ML-driven control strategies

About the Company

QuEra focuses on developing computational methods for in-the-loop stabilization of neutral atom Logical Quantum Processing Units (LQPUs), sitting at the interface of quantum hardware and control systems.