AI-Driven Quantum Sensing Research Associate

Heriot-Watt University; City of Edinburgh; Hybrid; GBP 38,000 - 47,000; Full time

Job summary

Heriot-Watt University is seeking a Research Associate for AI Algorithms for Spin-Based Quantum Technology. The role focuses on developing real-time AI methods to autonomously optimise spin-based quantum sensors and push quantum sensing towards transformative applications.

The post involves collaboration with quantum sensing researchers, development of Bayesian experimental design and reinforcement learning algorithms, and supervising students while pursuing high-impact publications.

Qualifications

• A PhD in computer science, electrical engineering, theoretical physics or related subjects (or expected to be awarded soon).
• Strong grasp of probability theory, Bayesian inference and machine learning.
• Proven track-record during PhD or post-doctoral work in modelling, simulations, algorithms and peer-reviewed publications.
• Experience in programming for data acquisition and analysis (C++, Python or MATLAB).

Responsibilities

• Develop robust multi-parameter quantum sensing protocols based on reinforcement learning.
• Collaborate with experimental colleagues on implementations of protocols on state-of-the-art quantum control hardware.
• Publish high quality research in peer-reviewed journals and conferences.
• Lead and supervise junior group members and PhD students; contribute to internal and external collaborations.

Skills

Probability theory

Bayesian inference

Machine learning

Research ability

Education

PhD in computer science, electrical engineering, theoretical physics or related subjects

Tools

C++

Python

MATLAB

Job description

Heriot-Watt University is seeking a Research Associate for AI Algorithms for Spin-Based Quantum Technology. The role focuses on developing real-time AI methods to autonomously optimise spin-based quantum sensors and push quantum sensing towards transformative applications.

The post involves collaboration with quantum sensing researchers, development of Bayesian experimental design and reinforcement learning algorithms, and supervising students while pursuing high-impact publications.