PhD in Control Theory for Learned Operators in Dynamical Systems
Develop mathematical foundations for reliable and robust operator-learning models in complex engineering systems.
Best for:Best for candidates with a strong background in systems and control theory, applied mathematics, and a keen interest in scientific machine learning.
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 their application in high-tech, medical, and energy sectors. The research involves bridging modern machine learning with classical control theory to ensure stability, robustness, and effective controller design. 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, numerical implementation, and practical validation.
What you'll do
- Develop new systems and control theories specifically for learned operators.
- 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 theoretical research with numerical implementation and validation.
- Validate developed methods using applications in high-tech, medical, and energy sectors.
- Contribute to teaching activities and student coaching (10-15% of time).
- Collaborate effectively within an interdisciplinary research team and with industrial partners.
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 in spoken and written English.
Instruction: English
- Strong analytical and mathematical skills.
- Experience with machine learning and artificial intelligence.
- Interest in combining theoretical research with numerical implementation.
- Ability to work independently and within an interdisciplinary team.
- Research-oriented attitude and curiosity for fundamental research.
- Must hold a master's degree or equivalent.
Funding & terms
How to apply
Deadline: 2026-10-18
Applications must be submitted through the official university portal. Ensure all required documentation is prepared according to the university guidelines.
- CV
- Cover letter
- Academic transcripts
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