This PhD position at Delft University of Technology (TU Delft) focuses on developing models to trace global material and financial flows, identify bottlenecks, and assess how resource and investment constraints shape pathways to a feasible clean energy transition. The research will address the critical role of technology readiness, mineral supply, and capital mobilization in the pace of the global energy transition, especially under volatile geopolitical conditions. Shortages, trade frictions, and financial mismatches can stall tipping dynamics and create carbon-intensive lock-ins. The successful candidate will develop an agent-based inspired module to map global flows of key materials (such as lithium, copper, nickel, tin), climate-finance streams, and trade relationships, deriving feasibility and lock-in indicators for alternative clean energy technology transitions. This module will be coupled to Integrated Assessment Models (IAMs) to quantify how resource scarcity, investment risk, and coordination failures reshape decarbonization pathways. The research will combine network analysis and agent-based modelling of economic systems to trace international material and financial interdependencies, supporting or limiting the feasibility of a clean energy transition. Key tasks include designing and implementing the agent-based model, analyzing quantitative indicators of feasibility, scarcity, and lock-in risk, coupling the module to existing climate-energy-economic models, and evaluating trade-offs across feasibility, equity, and robustness dimensions. The position is part of the ERC-funded RIPPLE project, led by Professor Giacomo Marangoni, within the Policy Analysis section of the Multi-Actor Systems department. The candidate will collaborate with an interdisciplinary team at the intersection of simulation, optimization, and policy modelling, and connect with TU Delft's Climate Action Programme. TU Delft offers a dynamic, international research environment with excellent facilities, mentorship, and tailored training for professional growth. The position includes a 4-year employment contract (split into two contracts), competitive salary, benefits, and support for relocation. Applicants must have a Master’s degree in a relevant field, proficiency in coding and quantitative analysis, interest in agent-based modelling and network analysis, and excellent English communication skills. Applications must be submitted online by 23 November 2025, including a CV, motivation letter, exercise on a peer-reviewed article, and diplomas/grade transcripts.