MSc Internship: Hybrid Quantum–Classical Optimization for Energy Systems

About this position

The energy transition requires advanced optimization methods to manage increasingly complex and flexible energy systems. At TNO, we explore how emerging computational approaches, including hybrid quantum–classical methods, can support more efficient and scalable decision-making in energy system operation and planning.

Internship | MSc Internship: Hybrid Quantum–Classical Optimization for Energy Systems

In this internship, you will contribute to the design and evaluation of optimization methods for a representative energy system application. The focus is on developing a clear and structured implementation, starting from a classical optimization model and exploring how hybrid or quantum-inspired approaches can be applied in a controlled and practical way. The project is designed as a feasibility and benchmarking study, with a well-defined scope to ensure a meaningful outcome within the internship period. The goal is to understand where advanced methods may add value, and where classical approaches remain more suitable.

This position can be carried out either as an MSc thesis project or as a regular research internship, depending on your university requirements and preferences.

You will join a multidisciplinary research team within TNO’s Energy & Materials Transition (EMT) unit, working at the intersection of energy systems, optimization, and emerging computation technologies. You will be supervised by experienced researchers and collaborate with experts in energy system modeling and system integration.

The internship offers exposure to applied research projects and insight into how advanced methods are translated into practical tools and decision-support frameworks.

Your responsibilities

• Formulating a representative optimization problem.
• Implementing a classical baseline model (e.g. MILP in Python).
• Exploring a decomposition or structured solution approach.
• Mapping part of the problem to a hybrid or quantum-inspired method (where applicable).
• Benchmarking and comparing approaches in terms of solution quality, runtime, and scalability.

The focus is on clear implementation, structured analysis, and critical evaluation, rather than on achieving performance breakthroughs.

What we expect from you

• Master’s student in Electrical Engineering, Applied Physics, Computer Science, or a related field.
• Solid background in optimization (e.g. MILP, operations research, convex optimization, or decomposition techniques).
• Proficiency in Python and scientific computing.
• Interest in energy systems and/or emerging optimization approaches (e.g. quantum or hybrid methods).
• Ability to work independently and structure technical work clearly.

Experience with energy system modeling or power systems is a plus, but not required.

What you'll get in return

• A highly professional, innovative internship environment, within a team of top experts.
• A suitable internship allowance (615 euro for wo-, hbo- and mbo-students, for a full-time internship).
• Possibility of eight hours of free leave per internship month (for a full-time internship).
• A free membership of Jong TNO, where you can meet other TNO professionals and join several activities, such as sports activities, (work-related) courses or the yearly ski-trip.
• Use of a laptop.
• An allowance for travel expenses in case you don’t receive an OV-card.

Security clearance

For this internship vacancy it is required that the AIVD issues a security clearance (VGB) after conducting a security screening. Take into account that this process may take about 8 weeks. If you have been abroad for more than 6 consecutive months, or if you do not have the Dutch nationality, it may take longer.

Contact

mojtaba.moradisepahvand@tno.nl