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This Position reports to:
R&D Team Lead

At the Corporate Research Center, we study and develop new technologies related to different fields and products; in the switching and systems team, we are focused on current interruption technologies and electrical systems.

Electric power distribution grids evolve rapidly and new modalities that include inverters, energy storage and AC/DC hybrid systems are becoming increasingly common. Such grids are typically dominated by power-electronic converters rather than rotating generators, which affects their resilience with respect to disturbances. This thesis aims to study the transient response and the resilience of converter-dominated grids through experiments in a miniature laboratory setup, and to model the grid response using machine learning.

Details:

• Suggested thesis period is spring semester 2026, or as agreed.
• The thesis work comprises 30 ECTS.
• The thesis is suitable for two students, or one student with adapted scope.
• The thesis will be conducted at ABB Corporate Research in Västerås, however some work can be done remotely. Accommodation in Västerås can be provided by ABB.

Your role and responsibilities

• Design and build a benchtop-size miniature power grid comprising inverter(s), transformer(s), motor(s), power switches, instrumentation and control.
• Develop a control and monitoring system based on NI-cRIO platform, preferably with a Python interface.
• Formulate a test plan to systematically study a set of switching events and the resulting transient response of the microgrid.
• Execute the test plan to gather data on the grid response to various switching events at different initial grid states.
• Build, train and deploy a machine learning model that predicts the transient response of the system following a switching operation.

Qualifications for the role

• Master-level engineering student in Electrical Engineering, Engineering Physics, Mechatronics, or similar.
• Knowledge in electric power systems and power electronics is beneficial.
• Hands-on experience from using machine learning regression is beneficial.
• Analytical and structured with a curious mindset.
• Open-minded approach to both theoretical considerations and hands-on work in the laboratory and with the resulting data.

More about us
Recruiting Manager Thomas Eriksson, +46 70 688 50 08, or supervisor Johan Nohlert, +46 72 226 83 18, will answer your questions.

Positions are filled continuously. Apply with your CV, academic transcripts, and a cover letter in English.

We look forward to receiving your application. If you want to discover more about ABB, take another look at our website www.abb.com.

A Future Opportunity
Please note that this position is part of our talent pipeline and not an active job opening at this time. By applying, you express your interest in future career opportunities with ABB.

We value people from different backgrounds. Could this be your story? Apply today or visit www.abb.com to learn more about us and see the impact of our work across the globe.