The power and energy systems community is increasingly interested in leveraging emerging computing technologies, including quantum computing, to address challenges in power system optimization, particularly in the context of the Energy Transition. Transitioning to a power system heavily reliant on weather-dependent renewable energy to achieve environmental targets introduces a critical dimension of uncertainty in power system operations, necessitating complex decision-making processes for system operators. Mixed-integer stochastic programming formulations are commonly used to model these uncertainties, but they pose computational challenges.

While some power system optimization problems have been adapted for quantum computing, current hardware limitations restrict their scalability and prevent us from studying large-scale problem instances where a potential quantum advantage may be observed. Instead, this project aims to develop a hybrid quantum-classical algorithmic framework, combining classical high-performance computing (HPC) and quantum processing units, to enhance the computational viability of mixed-integer stochastic programs in power system modeling. The focus will be on characterizing the convergence properties and potential computational advantages of these algorithms, as well as identifying their limitations, particularly in noisy quantum computing environments. Your work will involve experimentation with gate-based quantum computers to solve operational power system optimization problems.

You will primarily contribute to the Intelligent Energy Systems program within the Electrical Energy Systems group. This project is supported by SURF and, therefore, you will have the opportunity to interact with teams of experts from SURF.

• PhD in Electrical Engineering, Applied Physics, Operations Research, Computer Science or a related discipline with a focus on quantum algorithms and/or fundamentals of optimization techniques. The position is also open to candidates at the final stage of their PhD who have not defended yet, provided they can start no later than November 2024.
• Ability to conduct high quality academic research, demonstrated for instance by a relevant PhD thesis and/or publication(s).
• Proficiency in Python programming.
• Excellent command of the English language and good communication skills. Note that there is no Dutch language requirement.
• Be a team player and able to work in a dynamic, interdisciplinary context.

You must possess a comprehensive knowledge of quantum computation with an emphasis on optimization algorithms. You must have a proven ability to rigorously develop and apply quantitative decision-making methods (e.g., mathematical programming or data-driven/AI approaches), as evidenced by a relevant PhD thesis or publications. Furthermore, familiarity with topics such as surrogate modelling, mathematical decomposition methods and distributed optimization techniques is highly desirable. Familiarity with electrical power systems is also desirable but not necessary.

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

• Full-time employment for 1 year.
• Salary in accordance with the Collective Labour Agreement for Dutch Universities, scale 10 (min. €3,877 max. €5,090).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs on general skills, didactics and topics related to research and valorization.
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• Partially paid parental leave and an allowance for commuting, working from home and internet costs.
• A TU/e Postdoc Association that helps you to build a stronger and broader academic and personal network, and offers tailored support, training and workshops.
• A Staff Immigration Team is available for international candidates, as are a tax compensation scheme (the 30% facility) and a compensation for moving expenses