PhD Position Neural Network-Based Surrogate Models for Offshore Wind Foundations

Join the RAPID-Wind project to develop reduced-order and surrogate models with neural operators and geometric deep learning for offshore wind foundations

Job description 

We are looking for a highly motivated and talented PhD researcher to join the RAPID-Wind project, which aims to develop a new computational framework for the design of advanced offshore wind turbine foundations in deep waters. As turbine sizes increase and installations move to greater depths, the offshore industry faces growing challenges related to wave loading, dynamic response, and fatigue. Perforated monopiles are a promising concept to reduce hydrodynamic loads and increase passive damping, but their design requires fast and reliable prediction tools that can approximate complex free-surface, multiscale flow–structure interactions at a fraction of the cost of high-fidelity simulations. RAPID-Wind will develop a new computational modelling framework that enables high-fidelity simulations and near real-time predictions by combining adaptive numerical methods, high-performance computing (HPC), and efficient surrogate models based on reduced-order modelling (ROM) and neural operators. Note that there is another PhD position within the project focusing on the development of the underlying high-fidelity simulation framework; applicants with a primary interest in this topic are encouraged to apply via the corresponding link.

If selected, you will focus on developing reduced-order and surrogate models for the fast and accurate prediction of hydrodynamic loads and stress distributions in perforated offshore structures. The research will emphasize data-driven and learning-based ROM approaches for complex free-surface flow problems, combining numerical modeling with modern machine learning techniques. You will work with surrogate modeling concepts such as neural networks and neural operators, including approaches from geometric deep learning for handling complex geometries. In addition, you will investigate multi-fidelity and physics-informed training strategies to ensure robust and reliable predictions in data-scarce regimes. The reduced-order models developed in this PhD project will form a central building block for fast prediction and design exploration within the overall RAPID-Wind framework.

You will join the Numerical Analysis section at the Delft Institute of Applied Mathematics, in particular the SCaLA (Scalable Scientific Computing and Learning Algorithms) group, which develops scalable numerical methods for partial differential equations, reduced-order modeling, and scientific machine learning, with a strong focus on complex geometries and high-performance computing on modern CPU and GPU architectures. Your PhD project will be co-supervised by Alexander Heinlein (https://searhein.github.io/) and Oriol Colomés, lead of the Computational Multiphysics in Offshore Engineering (CMOE) group (https://tudelftcmoe.super.site/). You will work in close collaboration with the Offshore Engineering section in the Hydraulic Engineering Department, actively participate in regular group meetings, publish scientific articles, present your work at national and international conferences, and contribute to teaching and supervision activities within the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology.

A key aspect of this PhD project is close collaboration with industry partners to ensure that the research translates into real-world design practice. The research will be conducted in cooperation with companies and organizations leading the design and analysis of offshore wind foundations, and the definition of datasets and output quantities of interest for the reduced-order and surrogate models will be carried out jointly with these partners.     

Job requirements  

We are acutely aware that we are a diverse society and not every talented applicant will have had the same opportunities to advance their careers. We therefore pledge to fully account for any particular circumstances that the applicants disclose (e.g. parental leave, caring duties, part-time jobs to support studies, disabilities etc.) to ensure an inclusive and fair recruitment process that does not rely purely on common research metrics.

The successful applicant will have:

• A master’s degree in Applied Mathematics, Data Science, Machine Learning, Computer Science, Engineering, or another closely related field.
• Demonstrated expertise in machine learning and scientific computing, with a solid background in numerical methods for differential equations; experience with neural networks, surrogate modeling, reduced-order modeling, or scientific machine learning is expected.
• Strong programming skills in Python, with experience using modern machine learning frameworks such as PyTorch or JAX; experience with C++ or Julia is an advantage.
• A self-motivated, curiosity-driven mindset and openness to communication and collaboration.
• Excellent communication skills in English, both written and spoken.
• The ability to work independently and proactively within a multidisciplinary research team.

Doing a PhD at TU Delft requires English proficiency at a certain level to ensure that the candidate is able to communicate and interact well, participate in English-taught doctoral courses, and write scientific articles and a final thesis. For more details, please check the Graduate Schools Admission Requirements.

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.  

Will you need to relocate to the Netherlands for this job? TU Delft is committed to make your move as smooth as possible! The HR unit, Coming to Delft Service, offers information on their website to help you prepare your relocation. In addition, Coming to Delft Service organises events to help you settle in the Netherlands, and expand your (social) network in Delft. A Dual Career Programme is available, to support your accompanying partner with their job search in the Netherlands.  

Additional information
If you would like more information about this vacancy or the selection procedure, please contact Alexander Heinlen, via a.heinlein@tudelft.nl or Oriol Colomés  via j.o.colomesgene@tudelft.nl.     .

Application procedure
Are you interested in this vacancy? Please apply no later than 22 March 2026 via the application button and upload the following documents:

• CV including a list of publications (if any)
• Motivational letter describing your motivation and qualifications for the position (1 page)
• Qualification evidence including latest degrees and transcripts.
• A digital copy of MSc thesis (if applicable)

You can address your application to Alexander Heinlen.

Doing a PhD at TU Delft requires English proficiency at a certain level to ensure that the candidate is able to communicate and interact well, participate in English-taught Doctoral Education courses, and write scientific articles and a final thesis. For more details please check the Graduate Schools Admission Requirements. 

Please note:

• You can apply online. We will not process applications sent by email and/or post. 
• As part of knowledge security, TU Delft conducts a risk assessment during the recruitment of personnel. We do this, among other things, to prevent the unwanted transfer of sensitive knowledge and technology. The assessment is based on information provided by the candidates themselves, such as their motivation letter and CV, and takes place at the final stages of the selection process. When the outcome of the assessment is negative, the candidate will be informed. The processing of personal data in the context of the risk assessment is carried out on the legal basis of the GDPR: performing a public task in the public interest. You can find more information about this assessment on our website about knowledge security.
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