Doctoral Student in Machine Learning for Reliable Quantum Computing

About the role

Four-year fully funded PhD at KTH on machine learning for reliable quantum computing, using ML to optimise quantum control pulses, decode quantum error-correcting codes, and characterise noise in real quantum hardware. Deadline 8 June 2026.

Responsibilities

• Design ML methods for quantum error correction decoding, quantum control, and noise characterisation.
• Run experiments on real and simulated quantum hardware, in partnership with hardware groups inside and outside KTH.
• Publish in venues such as Physical Review X, Quantum, NeurIPS, and ICML.
• Complete the KTH doctoral coursework programme (60 ECTS).

Requirements

• Master’s degree in physics, computer science, applied mathematics, electrical engineering, or a closely related field.
• Strong background in quantum mechanics or machine learning, ideally both.
• Python proficiency; experience with Qiskit, Cirq, or PennyLane is a plus.
• Fluent English.

Nice to have

• Prior research on quantum error correction or quantum control.
• Familiarity with reinforcement learning or graph neural networks.
• A first-author paper or preprint in physics or ML.

How to apply

Search “Machine Learning for Reliable Quantum Computing” on KTH’s Varbi portal at the apply link. Submit CV, transcripts, research statement, Master’s thesis, and two references via Varbi before 8 June 2026.