Are you interested in contributing to address the current challenges faced by the high-tech industry in relation to the development of innovative approaches to smart maintenance in Industry 4.0? Are you fascinated by data-driven approaches, and curious about how the ever increasing amount of data can be exploited to design timely and 'just on time' maintenance plans?

PhD candidate in 'Data-Driven Predictive Maintenance of Complex Engineering Systems Through Robust Optimisation' (1.0 fte)
within the Operations, Planning, Accounting and Control (OPAC) Group of the Dept. of Industrial Engineering & Innovation Sciences, in collaboration with the Eindhoven Artificial Intelligence Systems Institute (EAISI).

The Eindhoven AI Systems Institute (EAISI) combines all TU/e Artificial Intelligence activities. Top researchers from various research groups work together to create new and exciting AI methodologies and applications with a direct impact on the real world. TU/e has been active in the field of AI for many years, which gives the new institute an excellent starting position to build upon.

Applicants should have completed (or be close to completion of) a Master degree in mathematics, operations management, operations research, econometrics, industrial engineering, or a closely related discipline, with a solid background in mathematical methods. Fluency in English is required.

The project

The high-tech industry is faced every day with the challenge of keeping their systems operational and maximize their availability whilst minimizing maintenance and operational costs. Predictive maintenance enables system downtime and costs to be minimized by acting before failures occur and grouping interventions to share set-up costs and possession time. By developing data-driven decision tools capable to extrapolate knowledge from different data sources and enable more reliable maintenance decisions based on data, with this project we aim at advancing knowledge in data-driven maintenance decision making.

In this project we aim to develop a smart maintenance decision framework for complex multi-component systems, specifically complex machines consisting of many heterogeneous maintainable units (components), which operate in an uncertain environment. Degradation, failure and repair are stochastic processes affected by uncertainty around operating conditions including environmental and usage factors.  We envision the framework to be smart and deal with uncertainty by combining learning and updating methods with decision models based on robust optimization to support predictive maintenance planning driven by data from alarms, sensors and process logs. Such a decision framework shall enable robust maintenance policies to be developed so as to mitigate the effects of uncertainty which characterize real-life operation of high-tech systems.

We expect the Ph.D. student to:

• develop the project proposal based on the most up to date relevant academic literature;
• combine data-driven approaches with robust optimization into mathematical models to support maintenance decision making under uncertainty;
• design algorithms to solve the built models;
• present the findings at conferences and publish papers in internationally renowned journals;
• communicate the results at events of EAISI.

• A meaningful job in a dynamic and ambitious university with the possibility to present your work at international conferences.
• A full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months.
• To develop your teaching skills, you will spend 10% of your employment on teaching tasks.
• To support you during your PhD and to prepare you for the rest of your career, you will make a Training and Supervision plan and you will have free access to a personal development program for PhD students (PROOF program).
• A gross monthly salary and benefits (such as a pension scheme, pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labor Agreement for Dutch Universities.
• Additionally, an annual holiday allowance of 8% of the yearly salary, plus a year-end allowance of 8.3% of the annual salary.
• Should you come from abroad and comply with certain conditions, you can make use of the so-called '30% facility', which permits you not to pay tax on 30% of your salary.
• A broad package of fringe benefits, including an excellent technical infrastructure, moving expenses, and savings schemes.
• Family-friendly initiatives are in place, such as an international spouse program, and excellent on-campus children day care and sports facilities.