PhD Positions in Computational Science, Machine Learning, and Offshore Wind Energy at Delft University of Technology

Delft University of Technology (TU Delft) is offering two fully funded PhD positions as part of the RAPID-Wind project, funded by the Dutch Research Council (NWO). The positions are supervised by Assistant Professor Alexander Heinlein and Oriol Colomés. The research focuses on the intersection of high-fidelity simulations, machine learning, and offshore wind energy, aiming to address real-world engineering challenges and contribute to the global energy transition. The first PhD position centers on high-fidelity simulations of innovative offshore wind foundations, leveraging advanced computational science techniques to improve the design and performance of wind energy infrastructure. The second position involves developing neural network-based surrogate models for offshore wind foundations, integrating machine learning approaches to accelerate and enhance engineering analyses. Applicants should have a strong background in computational science, engineering, mathematics, or related fields, with experience in high-fidelity simulations, machine learning, or numerical analysis considered a significant advantage. The ability to work collaboratively in a multidisciplinary environment and a motivation to contribute to sustainable energy solutions are essential. Proficiency in English is required, and further eligibility details are available on the official TU Delft careers pages. Both positions are fully funded, offering a competitive salary and benefits in accordance with Dutch PhD standards. The research is hosted at TU Delft, a leading institution in engineering and technology, located in the Netherlands. The application deadline is March 22, 2026. Interested candidates should review the detailed job descriptions and submit their applications via the official TU Delft online portal. For more information, candidates can contact the supervisors or visit the provided links. Key research areas include offshore wind energy, computational science, machine learning, high-fidelity simulations, neural networks, surrogate modeling, and the energy transition. The RAPID-Wind project provides an excellent opportunity for motivated students to engage in impactful research at the forefront of sustainable engineering.