PhD Machine Learning in Semi-Active Tuned-Mass-Damper Systems

Eindhoven University of Technology is an internationally top-ranking university in the Netherlands that combines scientific curiosity with a hands-on attitude. Our spirit of collaboration translates into an open culture and a top-five position in collaborating with advanced industries. Fundamental knowledge enables us to design solutions for the highly complex problems of today and tomorrow.

The Department of Mechanical Engineering department 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.

Introduction

Are you excited about the future of chips and the high-tech motion control industry? Would you like to combine and bring further the latest advances in machine learning and vibration control to improve current mechatronic system designs? Join us in this endeavor where data, machine learning, advanced vibration control, and high-tech mechatronics come together.

Job Description

The semiconductor roadmap is driven by throughput, accuracy, and costs. These drivers put ever stringent requirements on the high-tech mechatronic systems needed to produce chips, e.g., lithography machines like wafer scanners, but also wafer inspection tools and semiconductor assembly tools. All these tools consist of highly sensitive optical and measurement systems on the one hand, and tracking systems that support aggressive motion on the other hand. Needless to say that in these tools the undesired excitation of structural dynamics is to be avoided as much as possible. The resonances that remain being excited are often damped by the use of passive tuned-mass dampers or TMDs. The mass, stiffness and damping of these TMDs are matched with the properties of the structure that needs to be damped, and hence every module or functional component in these complex machines requires its own dedicated design. For the next generation of semiconductor equipment we envision active solutions that use a control loop based on measurements and/or predictions of the resonances with actuators to counteract the resulting vibrations. By exploiting the data available from online measurement through machine learning methods, these active solutions should become self-learning and able to adapt their dynamical properties as to withstand a priori unknown dynamic disturbances that potentially induce repetitive as well as non-repetitive vibrations.

Here is where you come in! Within the EAISI project MASC, which stands for hybrid and modular modelling and control for complex, flexible and interconnected systems in lithography applications, you will join the Dynamics and Control section at the Mechanical Engineering Department and help design, analyze, and test semi-active TMD systems with data-driven control strategies for active vibration control. That is, you will:

• Conduct a detailed literature study on active TMDs, noise/disturbance cancellation, and machine learning techniques within the scope of the problem.
• Formalize the constrained optimization problem in which energy dissipation in the TMD through control is maximized while satisfying constraints on maximum gains, stability margins, and limitations of the hardware, e.g., actuators, sensors, and its electro-mechanical components.
• Develop machine learning control strategies that through disturbance feedforward control and/or parameter-varying feedback control provide the ability to adapt the TMD dynamical properties on the spot as to deal with the disturbance situation at hand without compromising robust stability properties of the controlled system.
• Design (or modify an existing) semi-active TMD system to validate the developed control strategies and to test the optimization objective in meeting vibration cancellation specifications.

Job Requirements

Eligibility criteria:

• A master's degree in mechanical or electrical engineering (or equivalent).
• High-level English proficiency.

Required skills:

• A master's degree in systems and control, system dynamics, mechatronics or equivalent, with a thesis showing experience on data processing, modelling and analysis.
• Affinity with high-precision mechatronics and design principles.
• Programming skills in Matlab, Python, or similar.
• Strong conceptual and analytical skills
• Proven ability to undertake research.
• Demonstrable research, writing and presentation skills.
• The ability to work both independently and as part of a team.

Optional skills (preferred but not required):

• Knowledge of structural dynamics
• Familiarity with machine learning and/or system identification

English language requirements:

Proof of English language proficiency: You should meet either of these:

• C2 proficiency (formally known as CPE): minimum score of 180 (at least 169 per section)
• C1 advanced certificate (formally known as CAE): minimum score of 176 (at least of 169 per section)
• IELTS: Overall band score of at least 6.5 and a minimum of 6.0 for each section
• TOEFL: Overall band score of at least 6.5 and a minimum of 6.0 for each section

Doing a PhD at Eindhoven University of Technology requires good English proficiency to ensure that the candidate is able to communicate and interact well, participate in English-taught Doctoral Education courses, and write scientific articles and a final thesis. For more details, please check the Graduate School English proficiency requirements.

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. € 2,901 max. € 3,707).  
• 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.  
• 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. 

Information

Do you recognize yourself in this profile and would you like to know more? Please contact the hiring manager, Full Professor Marcel Heertjes, [email protected].

Visit our website for more information about the application process or the conditions of employment. You can also contact HR Services Gemini [email protected] or HR Advice [email protected].

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 publications list (if available), achievements relevant to the positions, etc.
• Contact information of two references.
• A copy of your master thesis.
• Bachelor and master courses grade lists, issued by the university where you got your degrees.

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.