Thesis Work: Machine Learning-based Identification of Electric Motors Parameters for Control
3 days ago
W ABB pomagamy przemysłowi wyprzedzać konkurencję - działać sprawniej i ekologicznie. U nas postęp to standard – dla Ciebie, Twojego zespołu i całego świata. Jako globalny lider dajemy Ci to, czego potrzebujesz, by to osiągnąć. Nie zawsze będzie łatwo, rozwój wymaga determinacji. Ale w ABB nigdy nie będziesz działać sam. Zarządzaj tym, co porusza świat.
Stanowisko to podlega:
R&D Department Lead
Take your next career step at ABB with a global team that is energizing the transformation of society and industry to achieve a more productive, sustainable future.
At ABB, we have the clear goal of driving diversity and inclusion across all dimensions: gender, LGBTQ+, abilities, ethnicity and generations. Together, we are embarking on a journey where each and every one of us, individually and collectively, welcomes and celebrates individual differences.
You will be part of Powertrain & Digitalization team at ABB Corporate Research in Västerås, Sweden. At Corporate Research we lead the innovation within ABB and our task is to ensure ABB's technology competitiveness now and in the future. We work in close collaboration with other research centers, our business areas: Motion, Process Automation, Robotics & Discrete Automation and Electrification, as well academic and industrial partners. In our creative and highly skilled team we develop, design, build and test new concepts and prototypes of physical and digital powertrains consisting of electric energy source, electrical motor, electric drive, and a selected application.
Details:
- Period:January - July 2026
- 30 ECTS per student
- Number of students: 1
Location: On site ABB Corporate Research in Västerås, Sweden or Hybrid
Your responsibilities:
- Conduct a state‑of‑the‑art review on machine‑learning methods for electric motor parameter identification.
- Develop, implement, and evaluate ML‑based parameter identification models in MATLAB/Simulink for a selected motor type.
- Design simulation studies and, where applicable, structured experiments to generate identification data under representative operating conditions.
- Compare ML approaches with classical/system‑identification baselines and quantify accuracy, robustness, and computational footprint.
- Analyze the effect of identified parameters on torque‑control performance, including steady‑state accuracy and dynamic response.
- Explore generalization strategies across operating points and model mismatch, including noise robustness and domain shift.
- Prepare clean, version‑controlled code, reproducible experiments, and clear documentation to enable handover.
- Summarize findings in a thesis report and presentation; contribution to a scientific publication is encouraged when results warrant it.
Qualifications for the role
- Currently pursuing a Master's degree in Electrical Engineering or a closely related field.
- Good working knowledge of MATLAB and Simulink.
- Interest in control theory and power electronics; familiarity with electric machines is beneficial.
- Familiarity with data‑driven methods and fundamentals of machine learning or system identification is an advantage.
- Theoretical grounding in signals and systems, statistics, and optimization will be beneficial for model design and evaluation.
- Strong interest in research and clear technical communication in English.
You are welcome to apply the by November 21. Please note that selection will be done on an ongoing basis and the position may be filled before last day of application.
We look forward to receiving your application (preferably in English).
Join us. Be part of the team where progress happens, industries transform, and your work shapes the world. Run What Runs the World.
Przyszła możliwość pracy Prosimy pamiętać, że to stanowisko jest częścią naszej bazy talentów i nie jest obecnie aktualnie otwartą ofertą pracy. Aplikując, wyrażasz zainteresowanie przyszłymi możliwościami kariery w ABB.
Cenimy ludzi z różnymi doświadczeniami. Zaaplikuj już dziś, aby wziąć udział w nadchodzących rekrutacjach i odwiedź stronę , aby dowiedzieć się, jak napędzamy postęp na całym świecie.
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