Quantum Computing Research Intern

M31 Biomedical AI develops foundation models and advanced computational approaches for medical imaging, clinical data, and other biomedical modalities. The Quantum Computing Research Intern will implement, benchmark, and evaluate quantum and hybrid quantum-classical methods for biomedical AI and scientific applications, while building reproducible research pipelines and communicating findings through publications and technical reports.

Responsibilities:

• Implement and benchmark. Build quantum and hybrid quantum–classical methods — variational quantum algorithms, quantum kernels, quantum neural networks, and emerging quantum ML approaches — and evaluate them against strong classical and deep learning baselines
• Run experiments. Design and run experiments on quantum simulators and, where appropriate, real quantum hardware
• Find the real applications. Explore where quantum computing could apply to biomedical AI — medical imaging, clinical and biological data, optimization, representation learning, and predictive modelling — working with AI scientists, ML engineers, clinicians, and external collaborators to identify problems where quantum methods may add genuine value
• Characterise the limits. Investigate noise, scalability, circuit depth, data encoding, computational cost, and hardware constraints
• Keep the work reproducible. Document and maintain reproducible workflows using Python, Git, and cloud-based research tools; review, debug, and improve research code
• Read, write, and explain. Conduct technical literature reviews, contribute to publications, technical reports and internal proposals, and communicate findings clearly to both technical and interdisciplinary audiences
• Work with AI scientists, machine learning engineers, clinicians, and external research collaborators to identify problems where quantum approaches may provide meaningful scientific or computational value
• Contribute to research publications, technical reports, internal research proposals, and presentations
• Clearly communicate complex quantum computing concepts and experimental findings to both technical and interdisciplinary audiences