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Alexander Heinlein

Top university

8 months ago

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

Degree Level

PhD

Field of study

Computer Science

Funding

The positions are funded by the Dutch Research Council (NWO) as part of the RAPID-Wind project. Funding typically includes a full-time salary and benefits according to Dutch PhD standards. Details on stipend amount, tuition coverage, and other financial aspects can be found on the official TU Delft careers pages.

Deadline

Expired

Country flag

Country

Netherlands

University

Delft University of Technology

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Keywords

Computer Science
Machine Learning
Mechanical Engineering
Mathematics
Numerical Analysis
Wind Energy
Computational Science
Artificial Neural Network
Energy Transition
Surrogate Modeling

About this position

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.

Funding details

The positions are funded by the Dutch Research Council (NWO) as part of the RAPID-Wind project. Funding typically includes a full-time salary and benefits according to Dutch PhD standards. Details on stipend amount, tuition coverage, and other financial aspects can be found on the official TU Delft careers pages.

What's required

Applicants should have a strong background in computational science, engineering, mathematics, or a related field. Experience with high-fidelity simulations, machine learning, or numerical analysis is highly desirable. Candidates should possess excellent analytical and problem-solving skills, and be motivated to contribute to scientific advances and sustainable energy solutions. Proficiency in English is required. Specific degree requirements and further details are available on the official application pages.

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Professors

Assistant Professor Numerical Analysis

University Name
Country flag

Netherlands

Research Interests

Fluid Mechanics

Aerodynamics

Discontinuous Galerkin

Viscous Flow

Multiscale Modeling