PhD on SecReSy4You Hybrid Physics-ML Anomaly Detection, Risk Assessment, and Attack-Tolerant Control with Probabilistic Guarantees for Networked Cyber-Physical Systems (NCPSs)

Introduction

Are you inspired by the challenge of making cyber-physical systems secure and resilient in the presence of uncertainty and cyber-physical attacks
Then you may be our next PhD candidate in resilient and learning-based control of cyber-physical systems within the SecReSy4You MSCA Doctoral Network at Eindhoven University of Technology.

Job Description

The Dynamics and Control group at Eindhoven University of Technology (TU/e) conducts world-class research aligned with the technological interests of the high-tech industry in the Netherlands with a focus on the Brainport region. Our goal is to produce engineers who are both scientifically educated and application-driven by providing a balanced education and research program that combines fundamental and application aspects. We equip our graduates with practical and theoretical expertise preparing them optimally for future challenges.

This PhD position is part of the SecReSy4You MSCA Doctoral Network which focuses on developing next-generation methods for security and resilience of cyber-physical systems (CPSs) with emphasis on resilient control learning-enabled systems and system-level assurance under adversarial conditions.

The SecReSy4You network brings together 10 doctoral candidates across leading European universities and industrial partners forming a highly collaborative and interdisciplinary research environment. As part of the program the PhD candidate will engage in international secondments with partner institutions and companies gaining exposure to both academic and industrial settings. The network also offers a rich program of training schools workshops and demonstrators providing opportunities to develop technical expertise build a professional network and contribute to cutting-edge applications in cyber-physical systems security.

Within this framework the PhD candidate will contribute to Work Packages 2 and 3 focusing on the development of data-driven and learning-informed control methodologies for networked cyber-physical systems (NCPSs).

Modern CPSssuch as industrial automation systems autonomous platforms and critical infrastructureare increasingly exposed to cyber-physical attacks and uncertainties. These disturbances induce complex time-evolving performance degradation that requires tightly integrated approaches combining control learning and uncertainty quantification.

This project develops a data-driven control framework grounded in first-principles models with emphasis on:

• Data-driven practical feedback linearization enabling control of nonlinear systems under uncertainty and partial model knowledge
• Learning dynamics within control loops integrating adaptive and optimization-based updates (e.g. stochastic gradient methods and Bayesian learning)
• Probabilistic performance guarantees leveraging tools from stochastic systems RKHS-based learning and Bayesian inference to certify performance and quantify uncertainty
• Attack-tolerant and resilient control design explicitly accounting for disturbances adversarial inputs and model mismatch
• Hybrid physicsML monitoring mechanisms to support detection and isolation when required.
• Research Objectives

The successful PhD candidate will work on:

• Data-driven nonlinear control under uncertainty
• Developing control strategies based on practical feedback linearization with limited or imperfect models.
• Learning-enabled control dynamics
• Embedding optimization and learning algorithms (e.g. SGD Bayesian updates) into control design and analysis.
• Attack-tolerant resilient control
• Designing controllers that maintain performance under faults and cyber-physical attacks.
• Probabilistic guarantees and uncertainty quantification
• Establishing guarantees on stability safety and performance using RKHS-based methods stochastic analysis and Bayesian frameworks.
• Supporting monitoring mechanisms
• Designing anomaly detection tools that complement control strategies when required.
  
  

Expected Outcomes

Mid-term outcomes include:

• Methods for data-driven control of nonlinear CPSs under uncertainty
• Algorithms integrating learning dynamics with control design.

Final outcomes include:

• Attack-tolerant control frameworks with probabilistic performance guarantees
• Certified resilience of CPSs using learning-based approaches.

This PhD is supervised by Carlos Murguia and Nathan van de Wouw.

Job Requirements

• A masters degree (or an equivalent university degree)in systems and control mechanical engineering electrical engineering or applied mathematics.
• Strong background in mathematical systems and control theory.
• Keen interest in hybrid dynamical systems control and cyber security.
• Knowledge of at least one programming language: Matlab Python is expected.
• Eager to work within a team and independently.
• Ability to collaborate with industry and academic researchers.
• Fluent in spoken and written English.
  
  

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 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. 3059 - max. 3881).
• 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 childrens day care and sports facilities.
• Unlimited access to the modern oncampus 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 13000 students and more than 7000 staff forming a diverse and vibrant academic community.

Our university is located in Brainport Eindhoven a worldleading tech region with more than 7000 hightech 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. Carlos Murguia .

Visit our website for more information about the application process. You can also contactHR Advice or.

Curious to hear more about what its 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.