Research assistant / research associate

Position overview

The research assistant (RA) / research associate will support focused projects in systems, control, and energy engineering. RAs work within an agreed research plan, while research associates take greater ownership of methods, validation, publications, and technical coordination.

The appointment level will reflect the applicant’s academic preparation, research experience, and the degree of independence required by the project.

Research topics

• Control synthesis, model predictive control (MPC), and numerical optimization.
• System identification, state estimation, data-driven modelling, and digital twins.
• Renewable microgrids, power-electronic converters, batteries, and resilient energy systems.
• Green-hydrogen production, storage, fuel cells, and Power-to-X systems.
• Embedded control, electric mobility, and industrial automation.

Key responsibilities

• Review relevant literature and standards, then translate the agreed research question into a practical work plan.
• Develop models, algorithms, and research software using MATLAB/Simulink, Python, C/C++, or suitable equivalent tools.
• Run simulations, analyse data, and support hardware-in-the-loop (HIL) or laboratory validation where required.
• Maintain reproducible code, data, configurations, results, and technical documentation.
• Contribute to reports, papers, presentations, and collaborative project meetings.

Required qualifications

• A BSc or MSc for RA consideration, or a PhD for research-associate consideration, subject to the official call.
• A degree in electrical, control, systems, computer, mechanical, energy engineering, applied mathematics, or a related field.
• A sound foundation in dynamical systems, control, optimization, numerical methods, or mathematical modelling.
• Evidence of programming, simulation, data analysis, or experimental engineering skills.
• Good written and spoken English, with the ability to work independently and in a research team.

Preferred experience

• MPC, robust or stochastic control, data-driven control, system identification, or state estimation.
• Digital twins, scientific machine learning, forecasting, fault diagnosis, or predictive maintenance.
• Power electronics, renewable microgrids, hydrogen systems, energy storage, or electric mobility.
• HIL or power hardware-in-the-loop (PHIL) testing and real-time or embedded platforms.
• A dissertation, publication, software repository, or experimental project demonstrating relevant work.