Postdoc: Hardware‑Aware Quantum Machine Learning Architectures

Syddansk Universitet; Odense; Presencial; DKK 420.000 - 480.000; Jornada completa

Descripción de la vacante

University of Southern Denmark invites applications for a one-year postdoctoral researcher position in Intelligent Computing Architectures, focusing on Hardware-Aware Quantum Machine Learning. The appointment runs for one year from March 1, 2027, with the possibility of extension subject to funding.

The successful candidate will design, optimize, and evaluate hybrid quantum-classical learning systems under realistic hardware constraints, exploring noise, connectivity, and resource overhead in

Formación

• PhD in Computer Science, Computer Engineering, Electrical Engineering, Quantum Computing, Machine Learning, or a closely related field completed within the last 6 years.
• Experience in quantum programming frameworks (Qiskit, PennyLane) including circuit design and simulation.
• Strong ML experience with modeling, training, and evaluation; hardware-aware ML is a plus.

Responsabilidades

• Design, implement, and simulate hybrid quantum-classical learning models under hardware constraints.
• Conduct reproducible experimental evaluations of learning performance, trainability, noise resilience, and computational resource requirements.
• Collaborate with Danish and international partners; contribute to proposals, publications, and project reports.

Conocimientos

Quantum programming

Machine learning

Team collaboration

English language

Educación

PhD in Computer Science/Engineering/ML or related

Herramientas

Qiskit

PennyLane

Simulation tools

Descripción del empleo

University of Southern Denmark invites applications for a one-year postdoctoral researcher position in Intelligent Computing Architectures, focusing on Hardware-Aware Quantum Machine Learning. The appointment runs for one year from March 1, 2027, with the possibility of extension subject to funding.

The successful candidate will design, optimize, and evaluate hybrid quantum-classical learning systems under realistic hardware constraints, exploring noise, connectivity, and resource overhead in