Postdoc on Machine Learning–Enhanced CFD for Wind-Energy Aerodynamic Optimization
Postdoc on Machine Learning–Enhanced CFD for Wind-Energy Aerodynamic Optimization
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Job Description
Departments Department of Built Environment
Eindhoven University of Technology is an internationally top-ranking university in the Netherlands that combines scientific curiosity with a hands-on attitude. Our spirit of collaboration translates into an open culture and a top-five position in collaborating with advanced industries. Fundamental knowledge enables us to design solutions for the highly complex problems of today and tomorrow.
Sustainability in its broadest definition is the cornerstone of research and education in the Department of Built Environment. We take the lead in (re)shaping the built environment to be future-proof safe healthy inclusive and respectful of planetary boundaries. We house the entire spectrum of technology engineering design and human behavior disciplines in the built environment with world-class experimental facilities at all scales. This allows us to address societal challenges from a uniquely integrated perspective.
Short introduction
Are you an innovative researcher with a strong background in CFD scientific machine learning (ML) wind energy and advanced optimization Join our team to develop cutting-edge solutions for aerodynamic design optimization of wind energy systems in complex urban environments.
Job Description
This research focuses on advancing cutting-edge aerodynamic design methodologies to significantly enhance wind energy harvesting in urban settings. The primary objective is to develop a high-fidelity CFDmachine learning (CFDML) framework capable of efficiently analyzing and optimizing rooftop aerodynamic duct structures for building-integrated wind energy systems. The aim is 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 advanced machine learning approaches including surrogate modeling and reinforcement learning to accelerate CFD optimization and enable adaptive control strategies for complex urban wind conditions. From an industrial standpoint the objective is to deliver a cost-effective and efficient solution that facilitates continuous decentralized power generation in densely populated urban areas.
The research outcomes are expected to contribute to both fundamental scientific knowledge and practical innovations in renewable close collaboration with IBIS Power the project will contribute to the further development of PowerNEST a modular rooftop system that integrates wind and solar energy. At this stage the focus is on optimizing the aerodynamic design of the PowerNEST duct structure which accelerates and guides the airflow toward the integrated turbines. The turbines are represented using simplified actuator models and are not explicitly included in the optimization process. This project will play a key role in translating advanced CFDML methodologies into practical design and control strategies helping unlock the full potential of urban wind energy integration. The selected candidate will be affiliated with Eindhoven University of Technology (TU/e) in the Netherlands with active engagement in the Eindhoven Institute for Renewable Energy Systems (EIRES) initiatives.
Job Requirements
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 Masters 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.
Conditions of Employment
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 addition we offer you:
- A contract for 1 year for 10 fte with good future perspectives.
- Salary in accordance with the Collective Labour Agreement for Dutch Universities scale 10 (min. 4241 max. 5538).
- 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 childrens 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.
Information
Do you recognize yourself in this profile and would you like to know more Please contact Prof. Hamid Montazeri
Visit our website for more information about the application process or the conditions of employment. You can also contact .
Are you inspired and would like to know more about working at TU/e Please visit our career page.
Application
We invite you to submit a complete application using the apply-button. The application should include a:
- Cover letter in which you describe your motivation and qualifications for the position.
- Curriculum vitae including a list of your publications and the contact information of three references.
- List of self-selected best publications that are relevant to this position.
- Copy of the candidates PhD Thesis (in English or in the original language accompanied by an English abstract or summary).
We look forward to receiving your application and will screen it as soon as possible. The vacancy will remain open until the position is filled.
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Employment Type
Full-Time
