Postdoctoral Position in Intelligent Computing Architectures

Syddansk Universitet; Odense; On-site; DKK 420,000 - 480,000; Full time

Job summary

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

Qualifications

• 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.

Responsibilities

• 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.

Skills

Quantum programming

Machine learning

Team collaboration

English language

Education

PhD in Computer Science/Engineering/ML or related

Tools

Qiskit

PennyLane

Simulation tools

Job description

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 integratingexpertisein 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 supportfor professional development through the Lecturer TrainingProgrammeand 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

Applicants should hold a PhD in Computer Science, Computer Engineering, Electrical Engineering, Quantum Computing, Machine Learning, or a closely related field (the degree should have been completed within the last 6 years at most):

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 research track record demonstrated through peer‑reviewed publications and the ability to work effectively in collaborative research environments. Experience in writing research proposals, funding applications, or project plans is highly desirable.

Team playerwith strongcollaboration skills and the ability to work effectively with academic, industrial, and technology‑development partners across Denmark and Europe.

All candidates mustdemonstrateexcellent verbal and written English skills, along withvery goodcommunication abilities and the ability to communicate technical results to multidisciplinary and international partners.

The successful candidate will join SDU Microelectronics , a dynamic and expanding section at the Institute of Mechanical and Electrical Engineering (IME). The section conducts internationally recognized research in integrated circuits, semiconductor technologies, spintronics, photonics, and advanced computing, and plays a central role in SDU’s ambition to be a national and European hub for semiconductor and chip innovation. The postdoc will work with Assistant Professor Alberto Marchisio on hardware‑aware quantum machine learning, with opportunities to connect this research to the section’s expertise in AI accelerators and energy‑efficient computing architectures.

For further information about the research and the position, please contact Assistant Professor Alberto Marchisio ( [e-mail] ), research contact and supervisor. Additional enquiries may be directed to Professor Farshad Moradi ( [e-mail] ), Head of the SDU Microelectronics section.

The application deadline is December 15, 2026, at 11:59 PM/23:59 (CET/CEST)

Appointment as a postdoc is temporary for an initial one-year period, and the preferred start date is from March 1, 2027.Extension may be possible depending on available funding.

Employment as a postdoc requires academic qualifications at PhD level at the time of employment.Employment will be in accordance with the collective agreement between the Ministry of Finance and the Danish Confederation of Professional Associations for academics in the state including the associated circular on the protocol for the job structure for academic staff at Danish universities and the provisions for postdoc as described therein.

Salary is determined in accordance with the applicable collective agreement and based on objective and gender‑neutral criteria, including the content of the position, responsibilities, and qualification requirements. As an applicant, you are entitled to information about the starting salary and salary range for the position. This information will be provided during the recruitment process. SDU does not collect information on applicants’ previous salary. Further information on salary and taxation.

Persons employed in the position may, based on a specific individual managerial assessment, be exempted from time registration, also known as a “self‑organizer”.

The University of Southern Denmark wishes its staff to reflect the surrounding community and therefore encourages everyone, regardless of personal background, to apply for the position. SDU conducts research in critical technologies, which, due to the risk of unwanted knowledge transfer, is subject to a number of security measures. Therefore, based on information from open sources, background checks may be conducted on candidates for the position(s).