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PhD in Control Theory for Learned Operators in Dynamical Systems

EURAXESS hosting organisation · Plusieurs pays européens

Are you fascinated by the intersection of control theory and scientific machine learning? Join us in developing the mathematical foundations needed to make deep operators reliable, robust, and applicable for control of complex engineering systems. In this PhD project, you will investigate how operator-learning models can represent dynamical systems, how their stability and robustness can be characterized, and how controllers can be designed for them. Your work will combine rigorous theory, numerical methods, and applications in high-tech, medical, and energy systems. Information Modern engineering increasingly relies on data-driven models to describe complex dynamical systems. Scientific machine learning is now enabling a new class of models that learn operators mapping system inputs and i

At a glance

Where: Multiple European countries
Deadline: 2026-09-25
Funding: EUR 3204 (parsed from page — verify)
Board: EURAXESS

Who can apply

Criteria stated by the funder on the official source.

Nationality

EURAXESS vacancies rarely print a nationality table. Confirm whether applicants from Tunisia can apply and whether a residence/work permit pathway exists on the employer page.

Non indiqué

Education

Specific Requirements - A master’s degree (or an equivalent university degree) in Systems and Control, Mechanical Engineering, Electrical Engineering, Applied Mathematics, or a closely related field. A research-oriented attitude. - Experience with and a strong interest in machine learning and artificial intelligence, and enthusiasm for combining these methods with systems and control theory. - Excellent analytical and mathematical skills, with an interest in developing new theoretical results. -

Non indiqué

Funding and what it does not cover

Funding details were taken from the official programme page text. Confirm the current call before applying.