The Life Cycle Engineering (LCE) research group of the Department of Mechanical Engineering at KU Leuven conducts research on sustainable product and process design across the full product life cycle. The group focuses on methodologies and technologies that support circular economy strategies such as reuse, repair, remanufacturing, and recycling.
Research activities combine ecodesign, manufacturing/robotic systems, software/computer vision tools, and sustainability assessment, and are carried out in close collaboration with industrial partners and public organizations. The group actively contributes to European and international research initiatives and provides a multidisciplinary research environment for doctoral and postdoctoral researchers working on the transition towards more sustainable production and consumption systems.
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Project
This PhD position is part of the academic network working on ecodesign, an international research and training initiative. The network aims to advance digital and ecodesign methodologies that enable circular economy practices at an industrial scale, in line with European sustainability, digitalization, and Digital Product Passport (DPP) policy objectives.
Digital Product Passports are emerging as a key policy instrument to improve transparency, traceability, and the circular performance of products throughout their lifecycles. A fundamental prerequisite for effective DPP implementation is robust product identification, enabling users and systems to reliably link physical products to their digital representations and up-to-date product data. However, most existing product identification solutions remain limited. Current identifiers, such as EPREL QR codes, typically provide static, model-level information, while more advanced approaches, such as the GS1 Digital Link, primarily define API standards predominantly used in logistics contexts. As a result, significant challenges remain for original equipment manufacturers to enable customers, service technicians, and remanufacturers to easily identify products at both model- and device-level, in a device-agnostic way, and to access product-specific, dynamic, and continuously updated information and instructions.
The objective of this PhD project is to develop AI-based component and product identification approaches and to investigate how (eco)labeling strategies can facilitate reliable access to product data in Digital Product Passports throughout the product lifecycle. The research will focus on developing and combining AI-enabled identification techniques, including text recognition, product image retrieval, and QR/2D code systems; investigating their integration across lifecycle stages; and exploring how (eco)labeling designs and standards can support interoperability, regulatory compliance, and circular practices.
- Progress beyond the state of the art
Going beyond static identifiers and single-modality solutions, this project will investigate how multiple AI-enabled identification approaches can be combined into a resilient identification framework. In addition, the research will explore how product identification can act as a gateway to dynamic and device-specific DPP data, considering emerging digital product data ecosystems and the role of labeling in encouraging circular behavior.
- Expected results and impact
The expected outcomes include AI models and software tools for automatic product identification across use, repair, and remanufacturing phases; guidelines for (eco)labeling and data sharing that facilitate DPP interoperability; and recommendations for policy making and standardization related to product identification and (eco)labeling.
- Validation and industrial relevance
The developed methodologies will be validated through industrial case studies in collaboration with European manufacturing and remanufacturing companies. For this, the PhD includes two mandatory international secondments (2-3 months), providing direct exposure to industrial design practice, advanced manufacturing technologies, and real world remanufacturing constraints.
Through this combination of methodological development, laboratory-based experimental validation, and industrial collaboration, the objective is to enable seamless product identification and data exchange that supports data-driven circularity.
Profile
• Education: You do not hold a doctoral degree. You hold a Master’s degree in software engineering, computer science, artificial intelligence, computer vision, or a closely related discipline, obtained with at least distinction (cum laude) or an internationally recognized equivalent, as required for admission to the KU Leuven Doctoral School.
• Technical skills: Practical experience in artificial intelligence, machine learning, computer vision, text recognition, and data-driven software systems.
• Communication: You communicate effectively in English, both orally and in writing; knowledge of Dutch is considered an asset, but is not required.
• Research focus: You have a strong interest in ecodesign, re‑ and demanufacturing, and human‑robot collaboration in an industrial context.
• Drive: You are motivated to conduct doctoral research and to work in a multidisciplinary and international research environment.
• Hands-on approach: You are willing to carry out experimental research and participate in mandatory international secondments at our industrial partners.
• International mobility rule: To foster international exchange, at the time of recruitment, you must not have resided or carried out your main activity (work or studies) in Belgium for more than 12 months during the 36 months immediately preceding 01/10/2026.
Beyond academic excellence, we value candidates with strong teamwork abilities, interdisciplinary curiosity, and an awareness of the socio-economic and policy impacts of sustainable engineering.
Offer
A funded PhD position, including international secondments, competitive salary, doctoral training, and access to state‑of‑the‑art human-robot cooperative labs equipped with various industrial computer vision systems. The EU Researcher Allowances will be used to cover both the employee's and the employer's mandatory charges
The network adheres to the European Charter for Researchers and the Code of Conduct for the Recruitment of Researchers, ensuring a transparent and fair selection process. We believe that ambitious research requires a variety of voices, and we view diversity as a core strength of our team.
Our recruitment is based on a broad definition of merit: we value not only academic grades but also your teamwork abilities, interdisciplinary knowledge, and soft skills. We are committed to equal opportunity and do not tolerate discrimination of any kind, including, but not limited to, ethnicity, gender identity, sexual orientation, age, religious diversity, neurodiversity, or socioeconomic background.
In line with our commitment to gender balance, we particularly encourage applications from women. We also welcome researchers from underrepresented backgrounds and new EU Member States to join our multidisciplinary environment.
To support our commitment to equity and meritocracy, please ensure you compile the “Summary Sheet” document (available here: https://kuleuven-my.sharepoint.com/:w:/g/personal/nuria_boixrodriguez_kuleuven_be/IQCOwYLom5SDTaRkTWtdT4b9AZD_-MJxaohko2Jk-Atq3AY?rtime=vLYz5G2v3kg) and include it as the first pages of your motivation letter. In addition, avoid using your family name or first name in the Motivation Letter and when naming your files (e.g., Summary_Motivation.pdf).
Interested?
For more information please contact Dr. Núria Boix Rodríguez, mail: [email protected].
KU Leuven strives for an inclusive, respectful and socially safe environment. We embrace diversity among individuals and groups as an asset. Open dialogue and differences in perspective are essential for an ambitious research and educational environment. In our commitment to equal opportunity, we recognize the consequences of historical inequalities. We do not accept any form of discrimination based on, but not limited to, gender identity and expression, sexual orientation, age, ethnic or national background, skin colour, religious and philosophical diversity, neurodivergence, employment disability, health, or socioeconomic status. For questions about accessibility or support offered, we are happy to assist you at this email address.
