PhD in Control Theory for Learned Operators in Dynamical Systems
Develop mathematical foundations for robust control of complex engineering systems using scientific machine learning.
Best for:Best for candidates with a strong background in systems and control theory who are eager to apply machine learning to complex dynamical systems.
Overview
This PhD position at Eindhoven University of Technology focuses on the intersection of control theory and scientific machine learning. The successful candidate will develop mathematical principles for learned operators, enabling reliable control of complex dynamical systems in high-tech, medical, and energy sectors. The research involves characterizing stability, robustness, and dissipativity directly in the operator domain while bridging modern machine learning with classical control theory. You will work within the Dynamics and Control group, collaborating with an interdisciplinary team of approximately 35 researchers. The role requires a blend of rigorous analytical theory and numerical implementation.
What you'll do
- Develop a new systems and control theory for learned operators mapping system inputs to responses.
- Establish mathematical principles for stability, contraction, and dissipativity in the operator domain.
- Design feedforward, feedback, and predictive controllers for systems represented by learned operators.
- Combine analytical theory with numerical methods to validate models on industrial applications.
- Contribute to an interdisciplinary research environment and collaborate with industrial partners.
- Participate in teaching activities for 10-15% of the employment duration.
- Coach students and contribute to the academic life of the Dynamics and Control group.
Requirements
Degree required: Master’s degree in Systems and Control, Mechanical Engineering, Electrical Engineering, Applied Mathematics, or a closely related field.
Degree offered: PhD
Language: English; C1 level proficiency.
Instruction: English
- Strong analytical and mathematical skills with an interest in theoretical research.
- Experience with and interest in machine learning and artificial intelligence.
- Ability to combine theoretical research with numerical implementation.
- Capacity to work independently and contribute to an interdisciplinary team.
- Fluency in spoken and written English (C1 level).
- Must hold a relevant Master's degree or equivalent university degree.
Funding & terms
How to apply
Deadline: 2026-10-18
Submit your application through the official university portal using the apply button. Ensure your submission includes a cover letter detailing your motivation and qualifications, along with a CV that lists your publications and contact information for three references.
- Cover letter
- CV
- List of publications
- Contact information for three references
Contact
Fahim Shakib· Assistant Professor
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