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.
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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 assessment, or design for disassembly/remanufacturing.
- Familiarity with cyber-physical systems, IoT integration, or learning factory environments.
- Experience working with SMEs.
- Experience with experimental design, statistical analysis, and benchmarking of AI algorithms on physical testbeds.
- Previous publications in peer-reviewed international journals or conferences.
Personal characteristics
- Research-oriented mindset with a strong interest in understanding the underlying mechanisms of phenomena.
- Independent, structured and able to take responsibility for your own research progress.
- Strong analytical and problem-solving skills, with the ability to work across disciplines.
- Excellent communication skills and the ability to convey complex ideas to both academic and industrial audiences.
- Modest, open to feedback and input from others, and able to respect, collaborate, and negotiate with industrial and academic partners.
Emphasis will be placed on personal qualities.
We offer
Evaluate and remove/add what is relevant for the position.
- An exciting job with an important mission in society, at the intersection of AI, advanced manufacturing, and sustainability.
- Developing tasks in a strong and international professional environment.
- Access to a state-of-the-art smart circular lab within the Learning Factory and to a network of SME industrial partners.
- Career guidance and follow-up during the PhD period
- Open and inclusive working environment with committed colleagues
- Favorable terms as a member of the Norwegian Public Service Pension Fund (SPK)
- Free Norwegian language training at a basic level (A2)
As a PhD Candidate at NTNU, you will have access to employee benefits.
Diversity
Diversity is a strength, and at NTNU we aim to be an employer that reflects the diversity in society and that makes use of the potential of the population's collective skills. Our vision is Knowledge for a better world and our values are creative, critical, constructive and respectful. We believe that an organization that is equal, diverse and gender-balanced is essential for us to achieve our goals.
We strive to attract employees with different skills, life experiences and perspectives to contribute to even better problem solving of our societal mission in research and education.
If you think this position is relevant and interesting, we encourage you to apply, regardless of gender, functional ability and cultural background, or whether you have been out of work for a period of time.
At NTNU we want to increase the proportion of women in scientific positions. We have a number of measures to promote equality.
Salary and conditions
In the position of PhD Candidate, code 1017, your gross salary will normally be NOK 550 800,-per annum depending on qualifications and seniority. A 2% statutory contribution to the State Pension Fund is deducted from the salary.
The employment period is 3 years for the doctoral work.
For employment as a PhD Candidate, it is a prerequisite that you gain admission to the PhD programme in Engineering PhD in Engineering (Doctoral Programme) 3 years - Trondheim - NTNU within three months of your employment contract start date, and that you participate in an organized doctoral programme through out the period of employment.
As an employee at NTNU, it is important that you keep yourself up to date with academic and organizational changes and adapt to them.
For the necessary professional and social interaction, it is a prerequisite that you are physically present and available to the institution on a daily basis.
The appointment is carried out in accordance with the principles of the State Employees Act, and Export control (legislation that regulates the export of knowledge, technology and services). Candidates who, after assessment of the application and attachments, are considered to bein conflict with the criteria in the latter act, will not be able to be employed.
About the application
The attachments (including a description of your scientific work) must accompany the application as these documents form the basis of the application assessment. The documents must be in English.
Please note: the application will only be assessed on the basis of the information we have received by the application deadline. Therefore, make sure that your application clearly shows how your skills and experience meet the criteria described above. The application and all attachments must be sent electronically via Jobbnorge.no. If you are invited to an interview, you must bring certified copies of certificates and diplomas upon request.
- CV, including a list of publications (if any), and contact information for two to three references.
- Transcripts and diplomas from Bachelor’s and Master’s degrees. Diploma Supplement is required for European Master Diplomas outside Norway. Chinese applicants are required to provide confirmation of the Master Diploma from China Credentials Verification (CHSI).
- A cover letter (motivation letter) of maximum two pages, describing your motivation, research interests, and how you meet the selection criteria. Your cover letter and research statement should reflect your own voice and ideas. If you use AI tools in preparing your application, please do so transparently and creatively — as a thinking aid rather than a substitute for your own reflection.
- A brief research statement (1–2 pages) outlining your preliminary ideas in relation to the project description.
- Copies of relevant academic works (Master’s thesis and any publications).
- Documentation of English proficiency, if applicable.
If all, or parts, of your education has been taken abroad, we also ask you to attach documentation of the scope and quality of your entire education, both Bachelor's and Master's education, in addition to other higher education. If your institution uses “diploma supplement” (normal for most European institutions), you must attach this. A description of the documentation required can also be found here. If you already have a statement from Norwegian Directorate for Higher Education and Skills (HK-dir), please attach this as well.
Joint work will be considered. If it is difficult to identify your contribution to joint work, you must attach a brief description of your participation.
When assessing the best qualified, we emphasize necessary qualifications such as education, experience and personal suitability. Motivation for the position, ambitions, and potential for research will also count when assessing the candidates.
NTNU recognizes a wide range of academic contributions and has committed itself to The San Francisco Declaration on Research Assessment and CoARA (responsible assessment of research and recognition of a greater breadth of academic contributions in accordance with NTNU's social mission).
General information
A public list of applicants with name, age, job title and municipality of residence is prepared after the application deadline. If you wish to be exempt from entry on the public applicant list, this must be justified. Assessment will be made in accordance with current legislation. You will be notified if the exemption is not granted.
If you think this position looks interesting and in line with your qualifications, you are welcome to apply.
Questions about the position can be directed to Associate ProfessorCarla Susana A Assuad e-mail: carla.assuad@ntnu.no
If you have any questions about the recruitment process, please contact HR Advisor Kristine Rognlid Hagaseth , e-mail: kristine.r.hagaseth@ntnu.no.
Application deadline: 26.07.2025
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For practical information about working at NTNU, please visit this webpage.
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Om arbeidsgiveren:
NTNU - knowledge for a better world
The Norwegian University of Science and Technology (NTNU) creates knowledge for a better world and solutions that can change everyday life.
Department of Manufacturing and Civil Engineering
Our profile of expertise can help to solve the world’s most important societal challenge: the development of sustainable solutions in industry and society. This profile includes research on modern industrial processes, use of new and recycled materials, new technological solutions and the application of new forms of organization and business models. The Department of Manufacturing and Civil Engineering is one of eight departments in the Faculty of Engineering.