Postdoctoral Fellow - Applied Machine Learning in Quantum Systems

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What the firm says 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 acro

公司表示 摘要 该职位专注于开发用于中性原子逻辑量子处理单元（LQPU）在环稳定化的计算方法。该职位处于量子硬件与控制系统的交界处，支持近期实验性能以及用于稳定、容错计算的长期控制架构开发。成功的候选人将设计并原型化最先进的方法，以实现可靠的量子操作，并将设备测量转化为可操作的控制反馈。职责包括推进测量信息反馈控制和机器学习驱动的推断等能力。关键职责 开发并部署机器学习模型，用于高保真量子操作推断和控制预测。设计并原型化在环控制机制，根据测量结果和系统状态自适应调整序列。与物理、量子纠错、硬件和控制团队合作，使用实验数据和系统级性能验证新的技术栈组件。必备资格 物理学、计算机科学、电气工程或相关领域的博士学位或同等经验，具有量子计算或量子物理的深厚背景。具有量子计算平台（中性原子、囚禁离子、超导量子比特或类似平台）的工作经验。具有将机器学习用于推断和硬件在环的实践经验。精通Python、C++或类似语言的编程技能。强大的分析和问题解决能力，能够承担技术责任。有效的沟通能力以及跨部门协作的能力。

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