PhD in Safe AI-based Control

Introduction

Are you fascinated by how machine learning can enhance control without compromising safety or stability? As a PhD candidate, you will develop scalable methods for expressive and flexible neural controllers for complex systems that are safe and verifiable by design.

Job Description

Neural networks can provide the flexibility needed to control increasingly complex dynamical systems, but their opaque and nonlinear behavior makes it difficult to guarantee safety and stability. How can we exploit the expressive power of machine learning without compromising the rigorous guarantees required in safety-critical control applications?

In this PhD project, you will develop methods for the analysis, verification, and design of neural-network-based controllers. Rather than treating safety and stability as properties to be assessed only after training, you will investigate safety- and verification-by-design approaches that incorporate certifiable properties directly into the controller architecture and learning process.

This project offers a unique opportunity to work at the intersection of machine learning and control theory. You will develop rigorous theory and scalable computational methods for certifying closed-loop safety and stability, with potential directions including control barrier and Lyapunov functions, quadratic constraints, semidefinite programming, and neural-network architectures with intrinsic stability or robustness properties. You will also investigate how physical insight, prior system knowledge, and stabilizing baseline controllers can be combined with learning to improve performance while retaining rigorous guarantees. The developed methods will be evaluated on benchmark problems and more realistic scenarios involving complex dynamical systems.

You will join the Control Systems Technology group in the Department of Mechanical Engineering. The group offers an open and collaborative environment that combines fundamental and applied research. Its expertise spans data-driven modelling of dynamical systems, control of complex and uncertain systems, motion control for high-tech systems, model predictive, networked, supervisory, neuromorphic, and learning-based control, cyber-physical systems, and optimization. This research is reinforced by close collaborations with industry. You will receive close scientific supervision while having the freedom to shape your research direction, present your work at international conferences, and develop as an independent researcher. Your work will contribute to the foundations of trustworthy artificial intelligence for control and support the reliable use of learning-based controllers in areas such as robotics, motion control, and energy systems.

Job Requirements

• A master’s degree (or an equivalent university degree)  in Systems and Control, Control Engineering, Engineering Cybernetics, Mechanical Engineering, Electrical Engineering, Computer Science, Mechatronics, Artificial Intelligence, or a closely related field.
• A strong interest in control theory, machine learning, and safety-critical systems.
• A research-oriented and mathematically rigorous mindset.
• Ability to work independently and in interdisciplinary collaborations.
• Motivated to develop your teaching skills and coach students.
• Fluent in spoken and written English (C1 level).

Conditions of Employment

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station.

In addition, we offer you: 

• Full-time employment for four years, with an intermediate assessment after nine months. You will spend a minimum of 10% of your four-year employment on teaching tasks, with a maximum of 15% per year of your employment. 
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. € 3,204 - max. € 4,051 gross base salary per month (full-time)).  
• In addition to your base salary, you will receive an 8% holiday allowance and an 8.3% year-end bonus, both calculated based on your annual gross base salary.
• As a TU/e employee, you participate in the ABP pension scheme, providing retirement pension and pension benefits for surviving dependents and occupational disability. TU/e pays 70% of the pension premium, while employees contribute the remaining 30%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process. 
• An excellent technical infrastructure, and on-campus children's day care.  
• Unlimited access to the modern on‑campus TU/e Student Sports Center at an exceptionally affordable rate.
• We support your wellbeing with free 24/7 access to OpenUp, providing you and your family with mental health support, expert guidance, and online training.
• An allowance for commuting, working from home and internet costs. 
• A Staff Immigration Team and a tax compensation scheme (the Expat Scheme) for international candidates. 

On our website you can discover even more information about our conditions of employment. Build on your career at TU/e!

About us

We are a leading international university where scientific curiosity meets a hands-on mindset. We work in an open and collaborative way with high-tech industries to tackle complex societal challenges. Our responsible and respectful approach ensures impact — today and in the future. TU/e is home to over 13,000 students and more than 7,000 staff, forming a diverse and vibrant academic community.

Our university is located in Brainport Eindhoven — a world‑leading tech region with more than 7,000 high‑tech companies and strong R&D activity. Known for breakthroughs in AI, photonics, semiconductors and advanced manufacturing, Brainport is a place where technology serves people and society. Learn more about the Brainport region here.

Information

Do you recognize yourself in this profile and would you like to know more? Please contact the hiring manager Dr. Patricia Pauli, Assistant Professor, [email protected].

Visit our website for more information about the application process. You can also contact HR Services, [email protected].

Curious to hear more about what it’s like as a PhD candidate at TU/e? Please view the video.

Are you inspired and would like to know more about working at TU/e? Please visit our career page.

Application

We invite you to submit a complete application by using the apply button. The application should include a:

• Cover letter in which you describe your motivation and qualifications for the position.
• Curriculum vitae, including a list of your publications and the contact information of three references. Kindly note that we may reach out to references at any stage of the recruitment process. We recommend notifying your references upon submitting your application.

Ensure that you submit all the requested application documents. We give priority to complete applications.

We look forward to receiving your application and will screen it as soon as possible. The vacancy will remain open until the position is filled.

Please note

• You can apply online. We will not process applications sent by email and/or post. 
• A pre-employment screening (e.g. knowledge security check) can be part of the selection procedure. For more information on the knowledge security check, please consult the National Knowledge Security Guidelines.
• Please do not contact us for unsolicited services.