PhD in Resilient Machine Learning and Formal Methods

Introduction

Are you passionate about the foundations of safe and resilient AI? Do you want to develop machine learning approaches that not only withstand adverse conditions but actively learn from their own failures – and can you back those systems with rigorous formal guarantees? We are looking for a motivated and talented PhD candidate to join a unique interdisciplinary project at the intersection of machine learning and formal methods.

Job Description

Machine learning models deployed in real-world environments inevitably face noisy data, distribution shifts, and situations their training never anticipated. Existing machine learning research focuses on limiting the impact of such disturbances, rendering such models robust. The PhD project aims to go the next step, closing the loop between failure and adaptation by developing resilient machine learning. The developed methods will detect when something has gone wrong, learn from failures and mispredictions, and recover to a stable and reliable operating state. Formal methods ensure that learning and recovery are performed with a provable quality of service.

The project is conducted in collaboration with two clusters at the Department of Mathematics and Computer Science of TU/e:

Data and Artificial intelligence. Novel learning algorithms will be developed, capable of operating in reactive, online settings where the data distribution may shift over time. Key challenges include detecting prediction failures, designing self-correcting update mechanisms that exploit past errors, and evaluating resilience empirically on challenging benchmarks. The project team has access to the national computing infrustracture, and TU/e HPC cluster SPIKE-1.

Formal system analysis. Rigorous formal methods will ensure that learnt machine learning methods and the resulting systems are indeed resilient, aiming at improved dependability and trustworthiness. Formal notions of resilience have to be developed along with algorithms to check resilience of machine learning models. Research is conducted in the fields of automated reasoning, probabilistic verification, and reinforcement learning to derive provable guarantees on resilience.

You will work at the interface of these two highly timely perspectives, contributing to both the algorithmic development and the formal analysis. The project involves regular collaboration among all three supervisors and will result in publications at top venues in machine learning, artificial intelligence, and formal methods. The position is embedded in the vibrant research environment of the newly established Center for Safe AI.

PhD candidate will be formally employed with the Data and AI cluster at the Department of Mathematics and Computer Science and supervised by Mykola Pechenizkiy, Cassio de Campos and Clemens Dubslaff.

Job Requirements

• MSc degree (or near completion) in computer science, mathematics, artificial intelligence, or a closely related discipline.
• A strong foundation in machine learning, including familiarity with statistical learning theory and probabilistic models; prior exposure to notions of robustness, resilience, or uncertainty quantification is an advantage.
• Mathematical maturity and experience with formal or rigorous reasoning; prior knowledge of formal methods, logics, or verification is beneficial but not required — genuine curiosity and willingness to develop these skills is essential.
• Good programming skills preferably in Rust or Python; practical experience with Python-based machine learning frameworks (e.g. PyTorch) is preferred.
• A motivated, creative, and self-driven working style with the ability to collaborate effectively in an interdisciplinary team.
• Motivated to contribute to teaching and to coach students as part of your professional development.
• Fluent in spoken and written English (at least 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. € 3,059 - max. € 3,881 gross per month).  
• A year-end bonus of 8.3% and annual vacation pay of 8%. 
• 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, on-campus children's day care and sports facilities.  
• Unlimited access to the modern on‑campus TU/e Student Sports Center at an exceptionally affordable rate.
• An allowance for commuting, working from home and internet costs. 
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) 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.

As one of the largest and most dynamic academic communities in the Netherlands, the department Mathematics and Computer Science (M&CS) brings together more than 140 scientific staff, over 250 PhD and EngD candidates, and more than 3000 students. We collaborate closely with leading industrial partners in the Brainport Eindhoven region and with universities across the globe—creating a uniquely fertile environment for both fundamental breakthroughs and applied innovation.

Information

Do you recognize yourself in this profile and would you like to know more? You can contact the supervisor directly (please mention [PhD Resilient ML] in the subject line of any enquiry):

• Mykola Pechenizkiy: [email protected]
• Cassio de Campos: [email protected]
• Clemens Dubslaff: [email protected]

Visit our website for more information about the application process.

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 (maximum 2 pages). Please include a brief statement on your experience with machine learning and/or formal methods
• Official grade transcripts of your BSc and MSc education, listing all courses taken and grades obtained.
• Curriculum vitae, including a list of your projects, publications, and any other relevant items
• A copy of or a link to your MSc thesis or an example of your academic writing if the MSc thesis cannot be shared.

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.