Postdoctoral Fellow - Applied Machine Learning in Quantum Systems

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About this role

Postdoctoral Fellow - Applied Machine Learning in Quantum Systems Boston, MA USA Summary This role focuses on developing computational methods for in-the-loop stabilization of neutral atom Logical Quantum Processing Units (LQPUs). The position sits at the interface of quantum hardware and control systems, supporting both near-term experimental performance and the long-term development of control architectures for stable, fault-tolerant computing. The successful candidate will design and prototype state-of-the-art methods to enable reliable quantum operations and translate device measurements into actionable control feedback. Responsibilities include advancing capabilities such as measurement-informed feedback control and machine learning-driven inference. Key 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 Required Qualifications - 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

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