Merle started as a collaborative PhD candidate at both the CML and the European Commission Joint Research Centre (JRC) in Sevilla in October 2026, aiding in the ACT! consortium project. Her research focuses on modelling the impacts of Circular Economy measures and aims to bridge the gap between bottom-up and top-down modelling approaches.

More information about Merle Praum

Professional experience

Merle obtained a Bachelor's in Econometrics and Operations Research at Maastricht University and continued with a MPhil and MSc in Business Data Science at the Tinbergen Institute, jointly hosted by Erasmus University Rotterdam, Universiteit van Amsterdam and Vrije Universiteit Amsterdam. She spent a semester abroad at the National University of Singapore. 

Her BSc Thesis on Game Theory modelling won the Natalies Best Bachelor Thesis prize. Her master thesis was in collaboration with the Amsterdam Medical Center (AMC) and employed Deep Learning for medication response predictions.

During her Master’s, her main interests were modeling, simulation, optimization, and prediction. Before starting her PhD studies, Merle worked as a scientific trainee at the European Commission’s Joint Research Centre in Seville. In this role, she analyzed the competitiveness potential of various circular economy technologies for the EU using Economic Complexity frameworks. 

In October 2026, Merle started a PhD at the CML and the European Commission Joint Research Centre (JRC) Sevilla, Unit of Circular Economy. 

Research topic

Material and resource use in present-day society is largely linear and extractive, systemically linked to climate

change, biodiversity loss, pollution, and resource scarcity. An accelerated transition to a circular economy

(CE) is needed to address these challenges. Merle's doctoral project develops methods to link bottom-up and top-down approaches into an integrated micro–macro modelling suite for circular economy policy support. This includes building detailed bottom-up stock and flow data; mapping regional and sectoral shifts in asset ownership, value-capture and diffusion potential of circular solutions; connecting these bottom-up datasets to dynamic top-down CGE models bidirectionally.