PhD - Intelligent Use and Processing of Technical Plastic Streams – Method Development and Validation Using End-of-Life Vehicle Parts

You develop a scientifically sound and transferable methodology for data-based decisions on the use and combination of technical plastic streams. You also develop and harmonize internal product, component, material, process, quantity and quality data as well as external databases, literature and supplier and partner information in a traceable data model. You generate virtual scenarios for the separation, bundling and mixing of available material sources as well as for combination with virgin material, taking into account differences between components, manufacturers and batches as well as temporal and quantity-related fluctuations. Furthermore, you develop data- and AI-supported models that evaluate material flow and mixing scenarios with regard to process stability, component quality, security of supply and uncertainty and derive prioritized recommendations. You determine the decision-relevant data requirements and assess whether statistical estimates, existing material and process models, rapid characterization or targeted tests provide relevant additional benefit. You validate the methodology using technical plastic components from end-of-life vehicles and a cross-manufacturer data model for end-of-life vehicle components and test its transferability to other end-of-product, post-consumer and post-industrial streams. Last but not least, you work closely with internal subject matter experts and external partners and document, present and publish your results.

• You develop a scientifically sound and transferable methodology for data-based decisions on the use and combination of technical plastic streams.
• You also develop and harmonize internal product, component, material, process, quantity and quality data as well as external databases, literature and supplier and partner information in a traceable data model.
• You generate virtual scenarios for the separation, bundling and mixing of available material sources as well as for combination with virgin material, taking into account differences between components, manufacturers and batches as well as temporal and quantity-related fluctuations.
• Furthermore, you develop data- and AI-supported models that evaluate material flow and mixing scenarios with regard to process stability, component quality, security of supply and uncertainty and derive prioritized recommendations.
• You determine the decision-relevant data requirements and assess whether statistical estimates, existing material and process models, rapid characterization or targeted tests provide relevant additional benefit.
• You validate the methodology using technical plastic components from end-of-life vehicles and a cross-manufacturer data model for end-of-life vehicle components and test its transferability to other end-of-product, post-consumer and post-industrial streams.
• Last but not least, you work closely with internal subject matter experts and external partners and document, present and publish your results.

• Completed university degree in materials science, plastics engineering, chemical engineering, mechanical engineering, data science, computer science or a comparable field with excellent results
• Sound knowledge in the field of materials science and data engineering; profound knowledge in scientific programming languages, especially Python; knowledge in machine learning, databases and data pipelines, uncertainty or robustness assessment, material characterization, compounding or evaluation of material and mixing scenarios are an advantage
• You enjoy working in a team, are reliable and communicate your results clearly; you work independently, analyze systematically and methodically; you structure complex relationships and decisions under uncertainty in a comprehensible way; you show high commitment and willingness to learn
• Enthusiasm for circular economy and data-based development of sustainable plastic solutions; interest in linking heterogeneous data sources and the resulting development of robust recommendations for source selection, bundling, mixing, virgin material content and quality assurance; enjoyment of analytical and experimental research activities
• Very good German and English in word and writing