About this role
This is NTNU NTNU is a broad-based university with a technical-scientific profile and a focus in professional education. The university is located in three cities with headquarters in Trondheim. At NTNU, 9,000 employees and 43,000 students work to create knowledge for a better world. You will find more information about working at NTNU and the application process here. Video: https://youtu.be/Xt-yHCN5QS0 About the position Are you motivated to take a step towards a doctorate and open up exciting career opportunities? As a PhD Candidate with us, you will work to achieve your doctorate, and at the same time gain valuable experience that qualifies you for a further career in higher education and research, in and outside academia. The Department of Manufacturing and Civil Engineering (IVB) at the Norwegian University of Science and Technology (NTNU) has a vacancy for a PhD Candidate in AI Transfer Learning for Design-to-Manufacturing in Circular Production Systems. The position is affiliated with the Production Management research group and offers a unique opportunity to work at the intersection of artificial intelligence, advanced manufacturing, and circular manufacturing in small and medium-sized enterprise (SME) contexts. The PhD project will be carried out within a smart circular lab integrated into our Learning Factory, which serves both as a research instrument and as a validation environment for industry-relevant case studies. The employment period is 3 years. Your immediate leader will be the Head of the Production Management research group. About the project Advanced digital technologies — including cyber-physical systems (CPS), artificial intelligence (AI), and the Internet of Things (IoT) — are reshaping manufacturing toward more sustainable, circular practices. While these technologies hold great promise, their integration across the full design-to-manufacturing system remains underexplored, particularly in data-scarce SME environments where product design and manufacturing must be co-optimized for multiple lifecycle iterations. This PhD project addresses three critical research gaps: (1) transfer learning for design–manufacturing integration; (2) AI optimization under circular constraints; and (3) validation and generalizability of learning factory findings to industrial practice. Main research question: How can transfer learning enable bidirectional knowledge flow between AI-driven product design and manufacturing optimization in circular economy systems under resource-constrained conditions? The research objectives are to: Develop and validate a transfer learning framework for bidirectional knowledge flow between product design and manufacturing AI models under circular constraints. Establish computational foundations for reinforcement learning under non-stationary material flows characteristic of circular systems. Create validated experimental protocols that enable learning factory findings to generalize to industrial SME practice. The work is organized in three phases: theoretical foundation and algorithm development (months 1–12); experimental implementation and validation on a physical testbed with 3–5 SME industrial partners (months 13–30); and analysis, generalization, and dissemination (months 31–36). Duties of the position Conduct independent research and contribute to the development of novel transfer learning architectures for AI-driven product design and circular manufacturing. Design, implement, and test algorithms on a physical smart circular testbed in our Learning Factory. Carry out industrial case studies in close collaboration with SME partners. Publish results in recognised international scientific journals and present them at international conferences. Contribute to teaching activities and to the development of the department’s new Master’s programme. Take part in the mandatory PhD research education programme. Be prepared for changes to your work duties after employment. Required selection criteria You must have an academically relevant Master’s degree in mechatronics engineering, Industrial Engineering, Manufacturing Engineering, Mechanical Engineering, Computer Science, Artificial Intelligence, Data Science, Industrial Design, Industrial Design Engineering, or a closely related field. Your course of study must correspond to a five-year Norwegian course, where 120 credits have been obtained at master’s level. Master’s students can apply, but they must obtain and document the master’s degree before starting the position. You must have practical industrial experience relevant to manufacturing, production, or engineering — obtained either through an internship, project work, or full-time employment in industry. You must have a strong academic background from your previous studies and an average grade from your Master’s degree, or equivalent education, which is equal to B or better compared to NTNU’s grading scale. If you do not have letter grades from previous studies, you must have an equally good academic foundation. If you have a weaker grade background, you may be considered if you can document that you are particularly suitable for a PhD education. You must meet the requirements for admission to the Faculty’s Doctoral Programme in Engineering. Documented programming experience (e.g. Python) and working knowledge of machine learning frameworks (e.g. PyTorch, TensorFlow, scikit-learn). You must have very good oral and written English skills. PLEASE NOTE: For detailed information about what the application must contain, see paragraph “About the application”. The appointment is to be made in accordance with NTNUs guidelines for recruitment positions for general criteria for the position. Preferred selection criteria Research or project experience in one or more of: transfer learning, reinforcement learning, deep learning, optimization under uncertainty, or digital twins. Knowledge of circular manufacturing, sustainable manufacturing, life cycle assessm