PhD Stipend within Dynamic Recipes for Sustainable Plastics

Aalborg University; Aalborg; On-site; DKK 320,000 - 420,000; Full time

Job summary

The PhD position at Aalborg University, Department of Materials and Production, Aalborg, Denmark, invites applications to design circular plastics systems by merging polymer processing with data-driven modelling. The three-year fixed-term role starts 1 December 2026 and includes merit for prior work.

You will perform hands-on lab work (extrusion, compounding, injection moulding), develop ML models, and collaborate with international partners to scale circular economy concepts from pilots to

Qualifications

• Master's degree required in engineering or science with strong technical/analytical foundation.
• Experience with Machine Learning and data analysis is desirable.
• Familiarity with polymer materials and processing (extrusion, compounding, injection moulding) is preferred.

Responsibilities

• Design circular plastics systems by combining polymer processing with data-driven modelling.
• Perform laboratory work ( extrusion, compounding, injection moulding ) and generate data for ML model training and validation.
• Collaborate with international partners to scale concepts and contribute to TRACE 2025 objectives.

Skills

Machine Learning

Data analysis

Polymer processing

English communication

Education

Master's degree in Virksomhedsteknologi or related field

Job description

In this PhD position, you will help design the next generation of circular plastics systems by combining hands-on polymer processing with data-driven modelling. The PhD study is a full-time, fixed-term position of three years, including merit for previous work, and is expected to start on 1 December 2026 or as soon as possible thereafter. The position is based at the Department of Materials and Production, Aalborg University, campus Aalborg, and is part of TRACE 2025: A Path to Resilience, Call 4: International Scaling of Proof-of-Concept Circular Economy Systems.

Your work tasks

In this PhD project, you will work at the intersection of polymer processing, materials science and Machine Learning to develop dynamic recipes for sustainable plastics. In a typical plastics production line, several types of materials are fed into the machines at the same time. These may include virgin materials from different suppliers, recycled content of varying quality, colourants, stabilizers and other masterbatches. Together with variations in processing parameters, this complexity makes it challenging to ensure that specific material properties remain stable from batch to batch.

You will explore how Machine Learning can be used as a practical tool to identify and adjust recipes that for a given machine and processing window fulfil defined target criteria. Building on existing evidence that Machine Learning can support recipe optimisation for well‑controlled materials and processes, you will investigate whether similar methods can be applied to mechanical recycling from heterogeneous waste streams. The ambition is to show how data-driven tools can support circular production based on inherently low-quality input materials, and how such tools can be adapted to contexts with limited waste management infrastructure.

Your work will include a substantial amount of laboratory activity related to polymer processing. You will prepare and process various blends using techniques such as extrusion, compounding and injection moulding, and you will test specific mechanical and physical properties in order to generate high-quality data for training and validating Machine Learning models. You will use these models to analyse how different feedstock compositions and processing conditions influence performance, and to evaluate the ability of the models to serve as robust tools for dynamic recipe design in real production environments.

The project is embedded in TRACE 2025’s ambition to scale Proof-of-Concept Circular Economy Systems internationally. You will contribute to understanding how circular plastics systems can move from isolated pilots to integrated, scalable solutions across sectors and geographies. As part of this, you will engage with partners in international pilots and contribute to comparative insights on enabling and hindering factors for scaling, including market conditions, policy frameworks and local infrastructures.

You will work closely with colleagues in the Materials Science and Engineering research group and collaborate with other departments at Aalborg University, including Energy, Biotechnology and Chemistry. International collaboration is a core element of the project, and you will be part of joint activities with the National Technical University of Athens, the University of Johannesburg in South Africa and partner universities in Nigeria. Through these collaborations, you will help document and share conditions for successful scaling of circular plastics systems and contribute to TRACE’s broader learning and impact framework.

As a PhD candidate, you will also be involved in the academic life of the department. You may take part in limited teaching activities, such as giving lectures in relevant courses or supporting project-based learning activities, and you will supervise or co-supervise student projects at bachelor and master level. You will participate in dissemination within TRACE, including meetings, workshops and learning activities, and you will attend project meetings in Denmark and abroad. The position offers opportunities for presenting your work at international conferences and for shorter research visits with project partners, subject to project needs and agreements.

You hold a master’s degree in Virksomhedsteknologi from Aalborg University or a similar degree in engineering or science that provides you with a strong technical and analytical foundation. Your educational background equips you to work with both materials and processes, and you are comfortable operating at the interface between experimental work and computational modelling.

You have experience with Machine Learning and data analysis and are motivated to apply these skills to complex materials and production systems. You are familiar with polymer materials and processing, and you have knowledge of techniques such as extrusion, compounding and injection moulding. You either already have experience with laboratory work on polymers or you are ready to develop this competence as a central part of your PhD project.

You communicate clearly in English, both in writing and speech, and you are prepared to work in an international environment with partners from different countries, disciplines and sectors. You approach scientific problems in a systematic way and you are able to document and analyse your results carefully, including handling experimental data for Machine Learning workflows.

As a person, you are collaborative and enjoy working in teams where expertise and perspectives differ. At the same time, you are able to work independently and take responsibility for planning and executing your own tasks. You thrive in an environment that is both international and cross-disciplinary, and you are comfortable moving between theoretical analyses, practical laboratory activities and discussions with partners from academia, industry and other organisations. You are at ease in a setting where you contribute to both scientific objectives and applied outcomes and where your work can have impact on how circular plastics systems are designed and implemented in practice.

Qualification requirements

PhD stipends are allocated to individuals who hold a Master's degree. PhD stipends are normally for a period of 3 years. It is a prerequisite for allocation of the stipend that the candidate will be enrolled as