PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition

PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition

Develop machine learning models to detect early signs of abrupt shift towards clean energy technologies and make climate action adaptive to this information.

Job Description

Positive tipping points in the innovation and diffusion of clean energy technologies can greatly accelerate progress towards a net-zero energy system. Yet, their emergence and timing remain difficult to anticipate and manage. This PhD will develop a machine learning module to detect early warning signals of positive tipping points from techno-economic data, helping policymakers design adaptive strategies for rapid and resilient decarbonization.

Your research will combine time-series analysis, supervised and unsupervised learning, and explainable AI methods to uncover the dynamic patterns preceding technological breakthroughs or large-scale adoption events. The module will be validated on both historical datasets and scenario data generated by Integrated Assessment Models (IAMs), large climate-economic models used for mapping future decarbonization pathways. Building on these insights, you will explore policy portfolios that are responsive to emerging tipping dynamics, assessing their trade-offs.

Your key tasks will include:

• Applying and comparing machine learning methods for time-series analysis and tipping point detection.
• Developing an interpretable machine learning framework to detect early warning signals of energy transition tipping points.
• Validating the new module with historical and scenario datasets.
• Designing adaptive policy portfolios responsive to tipping dynamics within a stylized IAM.
• Assessing trade-offs in transition outcomes, including economic feasibility, equity, and robustness.

Your Work Environment

You will carry out this research as part of the ERC-funded RIPPLE project, led by Professor Giacomo Marangoni, and embedded within the Policy Analysis section of the Multi-Actor Systems department. You'll collaborate with an interdisciplinary team working at the intersection of simulation, optimization, and policy modelling. You will also connect with TU Delft's strong internal network on climate action, particularly through the Climate Action Programme and its Climate Governance theme.

TU Delft offers you a dynamic, international research environment with excellent facilities and support for your academic and professional development. You will have access to state-of-the-art facilities, labs and equipment to help develop your research. The environment is open and collaborative, promoting daily interaction and knowledge exchange. With minimal hierarchical barriers, you’ll benefit from strong mentorship and tailored training. Professional growth is a priority, ensuring your success in academia.

Job Requirements

• A Master’s degree in a relevant field (e.g. Computer Science, Engineering, Policy Analysis, Economics, Environmental Science) with demonstrated proficiency in coding and/or quantitative analysis.
• Interest in machine learning, computational modelling, systems thinking, and climate-related challenges.
• Proficiency in at least one programming language used for scientific or analytical purposes, e.g., Python.
• Excellent communication skills in English, both written and verbal.
• A curiosity-driven mindset and a passion for doing research.

TU Delft (Delft University of Technology)

Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.

Faculty Technology, Policy & Management

The Faculty of TPM provides an important contribution to solving complex technical-social issues, such as energy transition, mobility, digitalisation, water management and (cyber) security. TPM does this with its excellent education and research at the intersection of technology, society and policy. We combine insights from both engineering and social sciences as well as the humanities. TPM develops robust models and designs, is internationally oriented and has an extensive network of knowledge institutions, companies, social organisations and governments.

Conditions of Employment

Doctoral candidates will be offered a 4-year period of employment in principle, but in the form of 2 employment contracts. An initial 1.5 year contract with an official go/no go progress assessment within 15 months. Followed by an additional contract for the remaining 2.5 years assuming everything goes well and performance requirements are met.

Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from €3059 - €3881 gross per month, from the first year to the fourth year based on a fulltime contract (38 hours), plus 8% holiday allowance and an end-of-year bonus of 8.3%.

As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills.

The TU Delft offers a customisable compensation package, discounts on health insurance, and a monthly work costs contribution. Flexible work schedules can be arranged.

Additional information

For more information about this vacancy, please contact Giacomo Marangoni via g.marangoni@tudelft.nl.