The Dynamics and Control (D&C) group at Eindhoven University of Technology (TU/e) is looking for a talented PhD candidate to work on developing analysis and synthesis tools for privacy-preserving fault detection schemes for networked Cyber-Physical Systems (CPS).
Technology companies today manufacture and sell high-tech equipment capable of measuring, processing, and transmitting operational data in real-time over the cloud. Companies collect data of the day-to-day operation of equipment to monitor the life and proper operation of their products. Online equipment monitoring allows them to forecast failures and schedule maintenance before a critical fault (event) occurs. However, sharing operational data might not be attractive to end-users as private/sensitive information of the products (services) they manufacture (provide) could be disclosed. There are two potential privacy threats in this setting: 1) the customer does not wish the manufacturer (service provider) to infer certain information (e.g., manufactured products specs); and 2) it is the communication channel itself (e.g., the internet), what the customer does not trust. Conversely, online equipment monitoring extends the life of equipment, decreases production bottlenecks, and prevents high and unexpected costs. That is, we have two opposite aspects here, customers would like to share data to improve performance but they do not want to share it for privacy reasons. It is therefore attractive for both parties (customers and manufacturers) to have masking mechanisms that allow for: 1) coding operational data before disclosure so that private information is hidden (in some appropriate sense); and 2) the coded data to be used by the manufacturer (service provider) to detect faults and schedule maintenance.
The broad goal of this project is to develop fundamental systems, control, and information theoretic tools that allow constructing privacy-preserving fault detection schemes for networked Cyber-Physical Systems. We aim at synthesizing real-time fault detection algorithms that, on the one hand, satisfy the required prescribed detection performance; and, on the other hand, guarantee a private exchange of system data over (potentially untrusty) communication networks. The main research questions to be addressed in the project are: Depending on the class of systems under study (e.g., linear, nonlinear, stochastic, hybrid, etc.), how to properly select privacy metrics that make sense from the point of view of dynamical systems? Given a particular privacy metric and a fault detection scheme, how to quantify the privacy level (information leakage) provided by the detection scheme? And, what synthesis tools can be used to systematically design coding functions and fault detection algorithms to maximize privacy and guarantee prescribed detection performance?
The Dynamics and Control (D&C) group at TU/e trains the next-generation of students to understand and predict the dynamics of complex engineering systems in order to develop advanced control, estimation, planning, and learning strategies which are at the core of the intelligent autonomous systems of the future. Autonomous vehicles, fully automated industrial value chains, high-tech systems, collaborative robots in unstructured environments, intelligent medical devices, automated transportation networks, soft robotics, together with sustainable automotive technology are key examples of the broad application domain of the (D&C) group. The design of these systems requires a thorough understanding of their underlying dynamics. Therefore, the first focal point of our research is on both data-based and first-principles-based modelling, model complexity management, and dynamic analysis of complex, multi-physics and multi-disciplinary engineering systems. Building on this foundation, our second focal point is on 'making autonomous systems smarter'. To this end, we develop both model- and data-based sensing, planning, and learning and control technologies to provide autonomous systems with the intelligence needed to guarantee performance, robustness, and safety. Combining the investigation on both dynamics and control theory in one section allows to take on these challenges standing in a privileged position. In particular, it enables us to educate uniquely skilled engineers and researchers as well as to valorize our research together with the high-tech, automotive and energy sectors.
Brainport Eindhoven Region
The Brainport Eindhoven is a world-class top technology region, in which companies, governments, and educational institutions (the so-called triple helix collaboration) work together on advancing technology for humanity. Brainport Eindhoven is among Europe's most prominent and innovative high-tech centers, where high-tech and design are combined with high-end manufacturing industry and entrepreneurship. Geographically situated in the southern part of the Netherlands, the Brainport region has a workforce of 400,000 people from all over the world working on high-tech solutions in areas such as health, mobility, energy, and nutrition. The region generates, by far, the most patents per thousand inhabitants in the world.
Do you recognize yourself in this profile and would you like to know more? Please contact Dr. Carlos Murguia, firstname.lastname@example.org
More information about terms of employment, can be found here
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to find out more about working at TU/e!Application
We invite you to submit a complete application by using the 'apply now'-button on this page. The application should include a:
- Cover letter in which you briefly describe your motivation and qualifications for the position;
- Curriculum vitae, including a list of your publications (if any) and the contact information of three references;
- MSc transcript indicating courses taken (including grades);
- Brief description of your MSc thesis;
- Proof of English language skills (if you are not a native speaker);
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We look forward to your application and will screen it as soon as we have received it. Screening will continue until the position has been filled.