"PhD on AI-driven Repair Recommendations for Sustainable Manufacturing"

Job Description

Are you passionate about developing intelligent algorithms that can support repair and remanufacturing decisions for sustainable manufacturing? As a PhD researcher, you will create innovative machine learning solutions to optimize the component lifecycle directly contributing to a more circular economy.

In the manufacturing landscape, determining whether a component should be repaired, reused, or discarded requires sophisticated condition assessment and decision-making capabilities. This PhD project tackles a critical challenge: how to develop robust machine learning models that can accurately predict component health and optimize repair decisions.

You will develop novel data-centric approaches to Remaining Useful Life (RUL) prediction that goes beyond traditional model-focused methods. You will focus on understanding and improving how data is collected, managed, and utilized throughout the entire process. Your research will encompass developing machine learning techniques that thrive with imperfect data, creating adaptive models that can quickly learn from new machines with minimal training data, and integrating these predictions with optimization algorithms to make cost-effective and environmentally sustainable decisions about component lifecycle management.

You will be part of the large ADD-reAM project, an NWO-funded consortium having 15 PhD researchers exploring complementary aspects of additive manufacturing, including technical design, logistics, sustainability assessment, and regulatory frameworks. Your PhD position will be embedded in the Information Systems group within the Department of Industrial Engineering and Innovation Sciences (IE&IS) at Eindhoven University of Technology (TU/e), collaborating closely with researchers working on predictive maintenance, operational decision-making, and artificial intelligence.

Requirements

• A master's degree (or an equivalent university degree) in Computer Science, Machine Learning, Operations Research or a related technical field.
• Strong background in deep learning with a motivation to advance fundamental techniques.
• Excellent analytical, problem-solving, and software engineering skills with prior experience implementing machine learning algorithms using well-known frameworks (e.g., PyTorch).
• Ability to work in an interdisciplinary team and interested in collaborating with industrial partners.
• Motivated to develop teaching skills and coaching skills.
• Strong communication skills, including proficiency in written and spoken English (C1).

Conditions of Employment

Fixed-term contract: 4 years. 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. € 3,059 max. € 3,881).
• 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.

Additional Information

For more information, please contact prof.dr.ir. Remco Dijkman at R.M.Dijkman@tue.nl or dr.ir. Zaharah Bukhsh at z.bukhsh@tue.nl. Visit our website for more information about the application process or the conditions of employment. You can also contact HRServices.IEIS@tue.nl.