This internship focuses on applying data-driven methods and Battery Digital Twin concepts to improve battery monitoring, performance prediction, and operational decision-making in robotic systems. Depending on the student's background and interests, the work may include battery-data processing, machine-learning model development, experimental testing, model validation, and visualization of results.
The position is open to master's and PhD students in robotics, artificial intelligence, computer science, electrical engineering, mechanical engineering, or a related field. Applicants should have experience with Python and an interest in machine learning, time-series analysis, batteries, or robotics; knowledge of PyTorch, battery modelling, or ROS is an advantage. The work will contribute to advancing the technology toward TRL 4-5 and support its longer-term development toward a future spin-off.
Contact for more information.
Type of internship: PhD internship, Master internship
Duration: 4-6
Required educational background: Computer Science, Electromechanical engineering, Energy
Supervising scientist(s): For further information or for application, please contact Parham Haji Ali Mohamadi (< e-mail verwijderd om veiligheidsredenen >)
The reference code for this position is 2026-INT-270. Mention this reference code in your application.
Only for self-supporting students.
Applications should include the following information:
Incomplete applications will not be considered.
The position is open to master's and PhD students in robotics, artificial intelligence, computer science, electrical engineering, mechanical engineering, or a related field. Applicants should have experience with Python and an interest in machine learning, time-series analysis, batteries, or robotics; knowledge of PyTorch, battery modelling, or ROS is an advantage. The work will contribute to advancing the technology toward TRL 4-5 and support its longer-term development toward a future spin-off.
Contact for more information.
Type of internship: PhD internship, Master internship
Duration: 4-6
Required educational background: Computer Science, Electromechanical engineering, Energy
Supervising scientist(s): For further information or for application, please contact Parham Haji Ali Mohamadi (< e-mail verwijderd om veiligheidsredenen >)
The reference code for this position is 2026-INT-270. Mention this reference code in your application.
Only for self-supporting students.
Applications should include the following information:
- resume
- motivation
- current study
Incomplete applications will not be considered.
