Research Fellow - Quantum Error Correction and AI

About the role

Nanyang Technological University (NTU) Singapore's College of Computing and Data Science is hiring a Research Fellow to join the QID Lab, led by Asst Prof Du Yuxuan. The lab sits at the intersection of quantum computing and artificial intelligence, covering quantum machine learning, quantum learning theory and AI for quantum science. The fellow joins a project under Singapore's AQAS programme, developing advanced AI, including large language models and neural decoders, for quantum error correction.

Key responsibilities

• Develop LLM-driven agentic systems that automatically discover and optimise quantum error-correcting codes such as quantum LDPC codes under realistic circuit-level noise and hardware constraints.
• Design scalable neural decoders such as Transformer-based foundation decoders, generalise them across code families and distances, and benchmark them against state-of-the-art classical decoders.
• Investigate AI-assisted synthesis and optimisation of fault-tolerant circuits, including syndrome extraction circuits and decoding for logical operations.
• Develop low-latency, hardware-efficient decoding methods such as model compression and FPGA deployment for real-time quantum error correction.
• Build and maintain open-source software and large-scale simulation pipelines on high-performance computing resources.
• Publish research in leading venues in quantum information and machine learning, and contribute to project reports, deliverables and grant proposals.
• Lead and mentor a team of PhD, Master's and undergraduate researchers, and coordinate day-to-day research activities.
• Collaborate with academic, industry and hardware partners within the AQAS programme and Singapore's quantum ecosystem.

Required profile

• PhD in Computer Science, Computer Engineering, Physics, Electronics Engineering or equivalent.
• Demonstrated capability to conduct innovative research, with first-author publications in leading journals and conferences (e.g. Physical Review Letters, Nature Communications, ICML, NeurIPS, ICLR).
• Proven ability to lead a research group, including supervising PhD and Master's students and undergraduate interns and coordinating multiple concurrent projects.
• Experience running large-scale computational experiments on HPC or GPU clusters.
• Ability to initiate collaboration research in multidisciplinary teams, and willingness to work some flexible hours.

Required skills

• In-depth knowledge of quantum error correction, fault-tolerant quantum computing and decoding algorithms.
• High-level expertise in deep learning (e.g. PyTorch), large language models and circuit-level QEC simulation.
• Ability to design and implement new computational and numerical methods, and to formulate hypotheses and experimental plans.