Summary
Aalborg University is offering a full-time, fixed-term PhD stipend for three years, focusing on the design of next-generation circular plastics systems. This project combines hands-on polymer processing with data-driven modeling, specifically Machine Learning, to develop dynamic recipes for sustainable plastics. The successful candidate will explore how Machine Learning can optimize recipes for mechanical recycling from heterogeneous waste streams, aiming to create scalable circular production solutions. The project is part of TRACE 2025 and involves international collaboration with partners in Greece, South Africa, and Nigeria.
Designation
PhD Fellow
Scholarship Details
| Category | Details |
|---|---|
| Title | PhD Stipend within Dynamic Recipes for Sustainable Plastics |
| Department | Department of Materials and Production |
| Employment Type | Full Time |
| Duration | 3 years (fixed-term) |
| Start Date | December 1, 2026, or as soon as possible thereafter |
| Ref Number | 2026/1123 |
Research Area
- Polymer processing
- Materials science
- Machine Learning
- Circular economy systems for plastics
- Sustainable materials development
Location
Aalborg University, Campus Aalborg, Denmark
Eligibility/Qualification
Competencies
- Master’s degree in Virksomhedsteknologi from Aalborg University or a similar engineering/science degree providing a strong technical and analytical foundation.
- Ability to work at the intersection of experimental work and computational modeling.
- Experience with Machine Learning and data analysis, motivated to apply these skills to complex materials and production systems.
- Familiarity with polymer materials and processing (e.g., extrusion, compounding, injection molding) or readiness to develop this competence.
- Clear communication in English (written and spoken).
- Prepared to work in an international environment.
- Systematic approach to scientific problems, careful documentation and analysis of results.
- Collaborative, enjoys teamwork, but also capable of independent work and task planning/execution.
- Comfortable in an international, cross-disciplinary environment, moving between theoretical analyses, practical lab work, and discussions with diverse partners.
Qualification Requirements
- Hold a Master’s degree.
- Enrollment as a PhD student at the Doctoral School of Engineering and Science.
- Completion of PhD courses (30 ECTS).
- Experience with teaching or other forms of knowledge dissemination.
- Completion of an external research stay (preferably 3-6 months at a foreign research institution).
Scholarship Description
This PhD project will focus on designing circular plastics systems by integrating polymer processing with data-driven modeling. The core task involves using Machine Learning to develop dynamic recipes for sustainable plastics, addressing the challenge of maintaining stable material properties despite variations in feedstock (virgin materials, recycled content, additives) and processing parameters. The research will investigate the applicability of Machine Learning methods to mechanical recycling of heterogeneous waste streams, aiming to demonstrate how data-driven tools can support circular production, even with low-quality input materials, and adapt to contexts with limited waste management infrastructure.
Key activities include substantial laboratory work: preparing and processing various polymer blends using techniques like extrusion, compounding, and injection molding; testing mechanical and physical properties to generate high-quality data for Machine Learning models; and using these models to analyze performance influences and evaluate their robustness for dynamic recipe design in real production environments.
The project is embedded in TRACE 2025, which aims to scale Proof-of-Concept Circular Economy Systems internationally. The PhD candidate will contribute to understanding how circular plastics systems can transition from isolated pilots to integrated, scalable solutions across sectors and geographies. This includes engagement with international pilot partners to gather comparative insights on enabling and hindering factors for scaling, such as market conditions, policy frameworks, and local infrastructures. Collaboration will extend to the National Technical University of Athens, the University of Johannesburg, and partner universities in Nigeria.
As a PhD candidate, you will also engage in academic life, potentially participating in limited teaching activities (lectures, project-based learning support) and supervising student projects. Dissemination within TRACE, attending project meetings (both in Denmark and abroad), presenting at international conferences, and undertaking shorter research visits with project partners are also part of the role.
How to Apply
Your application must include the following documents:
- Application stating reasons for applying and qualifications in relation to the position.
- Curriculum Vitae (CV).
- Diplomas (bachelor’s and master’s degree diploma, including grades).
- Other relevant documents.
The application must be submitted via Aalborg University’s recruitment system, accessible through the job advertisement on Aalborg University’s website.
Last Date for Apply
September 29, 2026
