Postdoctoral Position in Intelligent Computing Architectures

The Institute of Mechanical and Electrical Engineering at the University of Southern Denmark (SDU) invites applications for a one-year postdoctoral researcher position within 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 available funding.

We are seeking a highly motivated candidate to design, optimize, and evaluate hybrid quantum-classical learning systems under realistic hardware constraints. The project is platform-independent and will investigate how noise, limited quantum resources, device connectivity, and classical computational overhead affect learning performance and practical implementation.

Possible research directions include hardware-aware model design and training, quantum-classical ensemble learning and mixture-of-experts, resource-efficient learning methods, and automated architecture search. The specific research programme will be defined with the successful applicant according to their expertise and the project objectives, with a focused and achievable scope for the initial year. Evaluation will include suitable classical baselines and explicit assessment of computational resource requirements.

The successful candidate will work on model design, quantum programming, simulation, training, and experimental evaluation of hybrid quantum-classical learning systems. The position includes collaboration with academic, industrial, and technology-development partners, identifying relevant research and implementation opportunities, and supporting joint activities. The candidate will also contribute to research proposals and funding applications related to quantum machine learning, intelligent computing architectures, and resource-efficient computing, as well as publications, technical documentation, and project reporting. The research will be supervised by Assistant Professor Alberto Marchisio.

As a Postdoc in Hardware-Aware Quantum Machine Learning, your

key responsibilities
will be to:

• Design, implement, and simulate hybrid quantum-classical learning models, investigating the effects of noise, connectivity, finite sampling, resource limitations, and training conditions. Explore suitable methods such as hardware-aware training, ensemble learning, mixture-of-experts, or architecture search within the agreed research program.
• Conduct reproducible experimental evaluations of learning performance, trainability, noise resilience, and computational resource requirements using quantum programming frameworks and machine learning tools. Compare results with suitable classical baselines and correlate observed behavior with model design and hardware constraints.
• Support collaboration with Danish and international academic, industrial, and technology-development partners in quantum machine learning and intelligent computing architectures, identify relevant research and implementation opportunities, contribute to joint activities, and prepare research proposals, funding applications, publications, technical documentation, and project reports.

What we offer

• A stimulating research environment within SDU Microelectronics, a growing section integrating expertise in analog/digital IC design, spintronics, advanced computing, semiconductor technologies, and energy-efficient system architectures.
• An opportunity to develop a platform-independent research program in hardware-aware quantum machine learning, combining quantum programming, simulation, and machine learning methods with resource-aware evaluation.
• Opportunities for collaboration across SDU and with national and international academic and industrial partners in quantum machine learning, microelectronics, AI accelerators, and advanced computing.
• Strong support for professional development through the Lecturer Training Programme and mentoring, as well as opportunities to develop experience in research proposal writing, project development, and collaboration with Danish and European technology partners.
• An open and collaborative research culture within SDU’s Faculty of Engineering (TEK), at the Institute of Mechanical and Electrical Engineering (IME), supporting interdisciplinary research across electronics, computing architectures, quantum computing, and artificial intelligence.

What we expect

• Demonstrated experience in quantum programming using frameworks such as Qiskit, PennyLane, or equivalent tools, including implementation and simulation of quantum circuits.
• Demonstrated experience in machine learning, including model implementation, training, and evaluation, with strong programming skills. Experience in quantum machine learning, hybrid quantum-classical models, noise-aware optimization, ensemble methods, or automated architecture search is an advantage.
• A strong resear