Circular Economy Systems Scientist

Contributing to a circular world. Imagine it. Become a Circular Economy Modeller at TNO in Utrecht. We are looking for a skilled scientific modeler with experience of building software tools to advance the application of our research activities by improving our circularity assessment models. You will develop and apply models and tools which explore material flows, sustainability strategies and circularity policies using large datasets and Python-based tools. This is an opportunity to work in an established team of TNO scientists, software engineers and consultants, working together to answer some of society’s most challenging sustainability questions.

You will design and implement models to analyze circular economy scenarios within our established model suite. Taking established methods such as MFA, LCA, MRIO and applying them to a range of industry and policy questions. You will be translating complex scientific insights into actionable recommendations for business and policy stakeholders and collaborating with interdisciplinary teams in fields such as Plastics or Critical Raw Materials.

Together with an integral team of data and modelling experts, you will work at the intersection of sustainability topics (such as in Plastics or Critical Raw Materials), methodology (such as MFA & LCA) and scientific software development. You will develop and refine computational models to simulate circular economy scenarios and policy pathways. You will use Python packages and related tooling to build test and optimize these models, performing sensitivity analysis and scenario testing to explore the impacts of different policy or business interventions. You’ll be communicating findings in an engaging way for our industrial and policy stakeholders including visualizations of system performance and circularity pathways.

Your contribution to the group will also be in shaping the design and deployment of reusable tooling for our models. This will include handling complex technical, economic and material-specific datasets with metadata, in a way which supports robust scientific insights and outputs.