Postdoc Machine Learning-Enhanced CFD for Aerodynamic Optimization of Wind Energy Systems

Are you an innovative researcher with a strong background in computational fluid dynamics (CFD), scientific machine learning (ML), and renewable energy systems? Join our team to develop cutting-edge solutions for optimizing the aerodynamics of wind energy systems in complex urban environments.

Information
This research focuses on advancing state-of-the-art aerodynamic design methodologies to significantly enhance wind energy harvesting in urban settings. The primary objective is to develop a high-fidelity CFD–machine learning (CFD-ML) framework capable of efficiently analyzing and optimizing the coupled interactions among urban wind dynamics, rooftop flow structures, and vertical-axis wind turbines. With a focus on building-integrated wind energy systems, this project aims to push the boundaries of current technology by identifying optimal aerodynamic configurations that maximize wind capture efficiency and mitigate turbulence under diverse urban layouts and meteorological conditions. To achieve this, the project explores machine learning approaches—including surrogate modeling, and reinforcement learning—to accelerate CFD optimization and enable adaptive control strategies for complex urban wind environments. The ultimate goal is to deliver a scalable, high-performance solution that supports continuous and efficient decentralized power generation in densely populated areas.

The research outcomes are expected to contribute to both fundamental scientific knowledge and practical innovations in renewable energy. In close collaboration with IBIS Power, the project will contribute to the further development of PowerNEST—a modular rooftop energy system that captures both wind and solar energy to enable decentralized and continuous electricity generation in cities. This project will play a key role in translating advanced CFD–ML methodologies into practical design and control strategies, helping unlock the full potential of urban wind energy integration. The selected candidate will join the Building Physics group at Eindhoven University of Technology (TU/e) in the Netherlands, with active engagement in the Eindhoven Institute for Renewable Energy Systems (EIRES) initiatives.

We are looking for a candidate who meets the following requirements:

• A PhD degree in Aerospace Engineering, Mechanical Engineering, or a related engineering discipline.
• Solid knowledge of fluid mechanics, computational fluid dynamics (CFD), and optimization using machine learning techniques.
• A team player who enjoys coaching PhD and Master's students and working in a dynamic, interdisciplinary team.
• A proven ability to manage complex projects to completion on schedule.
• Excellent (written and verbal) proficiency in English, good communication and leadership skills.

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

• Full-time employment for 1 year.
• Salary in accordance with the Collective Labour Agreement for Dutch Universities, scale 10 (min. € 4,060 max. € 5,331).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs on general skills, didactics and topics related to research and valorization.
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• Partially paid parental leave and an allowance for commuting, working from home and internet costs.
• A TU/e Postdoc Association that helps you to build a stronger and broader academic and personal network, and offers tailored support, training and workshops.
• A Staff Immigration Team is available for international candidates, as are a tax compensation scheme (the 30% facility) and a compensation for moving expenses.

Do you recognize yourself in this profile and would you like to know more? Please contact the hiring manager Prof. Hamid Montazeri, h.montazeri@tue.nl.

Visit our website for more information about the application process or the conditions of employment.

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