Website https://twitter.com/tueindhoven Eindhoven University of Technology (TU/e)
Job description
**Project Introduction**
**Department**: Department of Mechanical Engineering
**Introduction**
Are you passionate about mathematical systems and control theory? Are you interested in the next generation of high-tech systems? The main challenge lies in the reliable operation of the entire system, where numerous interconnected modules influence each other. In this project, you will collaborate with an innovative industrial partner in the Brainport region to jointly design algorithms that ensure the reliable operation of semiconductor equipment. If all of this sounds fascinating to you, then this PhD position is tailor-made for you!
**Job Description**
We sincerely invite highly motivated students with a solid background in mathematical control theory and a strong interest in machine learning to apply for the PhD position in the Dynamics and Control group of the Department of Mechanical Engineering at Eindhoven University of Technology (TU/e).
The mission of the Dynamics and Control group is to conduct research and train the next generation of students on understanding and predicting the dynamics of complex engineering systems, thereby developing advanced control, estimation, planning, learning, and diagnostic strategies. These are the core of future intelligent autonomous systems: **designing and realizing intelligent autonomous systems for industry and society**.
Complex dynamical systems (such as semiconductor equipment) consist of numerous interconnected modules that are linked at functional, digital, and physical levels. The throughput of the equipment depends on continuous operation while meeting strict precision and performance requirements. Therefore, monitoring the health status of these systems is of utmost importance, and it is currently mainly performed by human experts. This PhD project aims to design innovative monitoring algorithms for complex dynamical systems, achieve fault isolation automation, and provide diagnostic performance guarantees.
Current health monitoring techniques are usually designed for the following two cases:
(i) individual system components/modules (which requires removing the component from the system in practice, but is often not feasible);
(ii) the entire system.
The first approach ignores the interactions between modules and their impact on the overall system. The second approach makes it difficult to isolate which module is failing and how to focus on the root cause section of the system that leads to the fault. This PhD position will address this challenge by developing **hierarchical diagnostic tools** targeted at complex dynamical systems.
The topics you will deeply investigate include:
– Interconnected dynamical systems
– (Distributed) observer design
– Data-driven methods
– Machine learning methods
– Fault detection and isolation
This PhD position is part of the “Holistic Design Automation for Semiconductor Manufacturing Equipment” project, which will recruit a total of 6 PhD students across the Departments of Mechanical Engineering, Electrical Engineering, and Mathematics and Computer Science at Eindhoven University of Technology. You will have the opportunity to collaborate with the semiconductor company ASML, apply research results in an industrial environment, and thus build a strong academic and industrial background.
You will have the opportunity to participate in the courses of the Dutch Institute of Systems and Control (DISC) and collaborate with industry in the Brainport region as well as academic researchers worldwide. By joining us, you will become part of a vibrant community of over 60 researchers (including faculty, postdocs, and PhD students) who conduct diverse research in the field of dynamical systems and control and their applications.
This PhD position is jointly supervised by Nathan van de Wouw, Tom Oomen, and Michelle Chong.
offer requirements
– A Master’s degree (or equivalent university degree) in Systems and Control, Mechanical Engineering, Electrical Engineering, or Applied Mathematics.
– A solid background in mathematical systems and control theory.
– Experience or strong interest in the fields of hybrid dynamical systems, observer design, learning techniques, and optimization.
– Proficiency in at least one programming language: Matlab, Python (expected requirement).
– Enthusiasm for teamwork and the ability to work independently.
– Ability to collaborate with industry and academic researchers.
– Fluent oral and written English communication skills.
offer benefits
Engage in meaningful work at a vibrant and ambitious university, in an interdisciplinary environment and international networks. You will work on a beautiful green campus within walking distance of the central train station. In addition, we offer you:
– A full-time 4-year contract, with an intermediate evaluation after 9 months.
– You will spend at least 10% of your time on teaching tasks during the four-year employment period, up to a maximum of 15% per year.
– Salary and benefits in accordance with the Dutch university collective labor agreement P-scale (minimum € 3,059 – maximum € 3,881).
– Year-end bonus of 8.3% + holiday allowance of 8%.
– High-quality training programs and other support to help you grow into an aware, autonomous scientific researcher. At TU/e, we encourage you to take control of your own learning process.
– Excellent technical infrastructure, on-campus childcare, and sports facilities.
– Commuting, home working, and internet expense allowances.
– Staff immigration team and tax compensation scheme for international candidates (30% tax ruling).
visit offer
To apply for this job email your details to HRadviceME@tue.nl