PhD Position Neural Network-Based Surrogate Models 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.

TU Delft (Delft University of Technology)
Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.

At TU Delft we embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.

Challenge. Change. Impact!

Faculty of Electrical Engineering, Mathematics and Computer Science
The Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) brings together three scientific disciplines. Combined, they reinforce each other and are the driving force behind the technology we all use in our daily lives. Technology such as the electricity grid, which our faculty is helping to make completely sustainable and future-proof. At the same time, we are developing the chips and sensors of the future, whilst also setting the foundations for the software technologies to run on this new generation of equipment – which of course includes AI. Meanwhile we are pushing the limits of applied mathematics, for example mapping out disease processes using single cell data, and using mathematics to simulate gigantic ash plumes after a volcanic eruption. In other words: there is plenty of room at the faculty for ground-breaking research. We educate innovative engineers and have excellent labs and facilities that underline our strong international position. In total, more than 1000 employees and 4,000 students work and study in this innovative environment.

Click here to go to the website of the Faculty of Electrical Engineering, Mathematics and Computer Science.

Postdoc Hyweld: Creating Thermoset-Thermoplastic Coupling Layers for Welding

Job description

To reduce aviation's climate impact and meet EU emission targets, lighter aircraft and faster adoption (and scaling-up!) of sustainable technologies are essential. One promising solution is using high-performance thermoplastic composites which offer advantages over traditional thermoset composites such as faster processing, higher production rates at lower cost, and recyclability. Whereas thermoset components are assembled with mechanical fasteners like rivets (additional weight and cost, and a major cause for production rework and scrap), thermoplastic composites are assembled through welding, which reduces weight and simplifies production.

HyWeld aims to develop hybrid welding technology that enables joining of thermoplastic and thermoset composite components, taking advantage of both the materials. A crucial first step in this process is to create a weldable thermoset surface.

The postdoctoral researcher will play a vital role in the development of this coupling layer used on the thermoset, to enable the welding technology to the thermoplastic substrate. The work will involve the assessment of compatibility of the coupling layer with the thermoset and thermoplastic materials used in the substrates. This entails research on diffusion, curing and adhesion mechanisms, interphase morphology studies, and optimization of the manufacturing cycle for optimal structural performance. The research will consist of both experimental and modelling work. Key responsibilities include experimental validation of the structural performance of the coupling layer, with models to predict the compability and optimal processing cycle for different material combinations, and contributing to scientific publications and project deliverables.

The position is based at Delft University of Technology, Faculty of Aerospace Engineering and is part of a close collaboration with SAM XL and several industrial partners.

Job requirements

• Educational Background: A PhD degree in aerospace engineering, mechanical engineering, material science, nanoscience, polymer chemistry, applied physics or a related subject, with a strong background in experimental work relating to polymer processing, fracture mechanics. Experience in developing constitutive models is considered an advantage.
• Critical Thinking and Scientific Leadership: Strong analytical and critical-thinking skills, with the ability to independently define research directions, and lead key technical aspects of the project.
• Teamwork and Responsibility: Ability to work effectively within a multidisciplinary project team while taking ownership of individual research objectives and deliverables.
• Communication Skills: Excellent proficiency in English, both written and oral, with the ability to communicate research outcomes clearly to academic and industrial stakeholders.

The selected candidate is expected to actively contribute to a dynamic and collaborative research environment within the Department of Aerospace Structures and Materials, Faculty of Aerospace Engineering at Delft University of Technology.

TU Delft
Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.

At TU Delft we embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.

Challenge. Change. Impact!

Faculty Aerospace Engineering
The Faculty of Aerospace Engineering at Delft University of Technology is a leading international community where innovation in aerospace meets global challenges. Our support and scientific staff, including PhD candidates, postdocs, and students, largely work together on three main themes: the energy transition, sustainable aerospace, and safety and security, with the aim of tackling climate change and contributing to the independence and security of Europe.

When you join us, you become part of a diverse, collaborative, and forward-thinking environment where your ideas and perspectives are valued. Our work extends beyond the lab—into field labs, innovation hubs, and partnerships with other faculties, research institutes, governments, and industry, both locally and globally.

We are committed to fostering an inclusive and welcoming workplace, assisted by an active Diversity & Inclusion team. This includes tangible support such as funding for extra personnel for family and caregiving responsibilities, mentoring programmes, and initiatives that promote cultural exchange and integration.

You don’t just join our faculty — you join a community where you can thrive, grow, and help shape the future of aerospace.

PhD Position Neural Network-Based Surrogate Models 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.

TU Delft (Delft University of Technology)
Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.

At TU Delft we embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.

Challenge. Change. Impact!

Faculty of Electrical Engineering, Mathematics and Computer Science
The Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) brings together three scientific disciplines. Combined, they reinforce each other and are the driving force behind the technology we all use in our daily lives. Technology such as the electricity grid, which our faculty is helping to make completely sustainable and future-proof. At the same time, we are developing the chips and sensors of the future, whilst also setting the foundations for the software technologies to run on this new generation of equipment – which of course includes AI. Meanwhile we are pushing the limits of applied mathematics, for example mapping out disease processes using single cell data, and using mathematics to simulate gigantic ash plumes after a volcanic eruption. In other words: there is plenty of room at the faculty for ground-breaking research. We educate innovative engineers and have excellent labs and facilities that underline our strong international position. In total, more than 1000 employees and 4,000 students work and study in this innovative environment.

Click here to go to the website of the Faculty of Electrical Engineering, Mathematics and Computer Science.

Postdoc Hyweld: Creating Thermoset-Thermoplastic Coupling Layers for Welding

Job description

To reduce aviation's climate impact and meet EU emission targets, lighter aircraft and faster adoption (and scaling-up!) of sustainable technologies are essential. One promising solution is using high-performance thermoplastic composites which offer advantages over traditional thermoset composites such as faster processing, higher production rates at lower cost, and recyclability. Whereas thermoset components are assembled with mechanical fasteners like rivets (additional weight and cost, and a major cause for production rework and scrap), thermoplastic composites are assembled through welding, which reduces weight and simplifies production.

HyWeld aims to develop hybrid welding technology that enables joining of thermoplastic and thermoset composite components, taking advantage of both the materials. A crucial first step in this process is to create a weldable thermoset surface.

The postdoctoral researcher will play a vital role in the development of this coupling layer used on the thermoset, to enable the welding technology to the thermoplastic substrate. The work will involve the assessment of compatibility of the coupling layer with the thermoset and thermoplastic materials used in the substrates. This entails research on diffusion, curing and adhesion mechanisms, interphase morphology studies, and optimization of the manufacturing cycle for optimal structural performance. The research will consist of both experimental and modelling work. Key responsibilities include experimental validation of the structural performance of the coupling layer, with models to predict the compability and optimal processing cycle for different material combinations, and contributing to scientific publications and project deliverables.

The position is based at Delft University of Technology, Faculty of Aerospace Engineering and is part of a close collaboration with SAM XL and several industrial partners.

Job requirements

• Educational Background: A PhD degree in aerospace engineering, mechanical engineering, material science, nanoscience, polymer chemistry, applied physics or a related subject, with a strong background in experimental work relating to polymer processing, fracture mechanics. Experience in developing constitutive models is considered an advantage.
• Critical Thinking and Scientific Leadership: Strong analytical and critical-thinking skills, with the ability to independently define research directions, and lead key technical aspects of the project.
• Teamwork and Responsibility: Ability to work effectively within a multidisciplinary project team while taking ownership of individual research objectives and deliverables.
• Communication Skills: Excellent proficiency in English, both written and oral, with the ability to communicate research outcomes clearly to academic and industrial stakeholders.

The selected candidate is expected to actively contribute to a dynamic and collaborative research environment within the Department of Aerospace Structures and Materials, Faculty of Aerospace Engineering at Delft University of Technology.

TU Delft
Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.

At TU Delft we embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.

Challenge. Change. Impact!

Faculty Aerospace Engineering
The Faculty of Aerospace Engineering at Delft University of Technology is a leading international community where innovation in aerospace meets global challenges. Our support and scientific staff, including PhD candidates, postdocs, and students, largely work together on three main themes: the energy transition, sustainable aerospace, and safety and security, with the aim of tackling climate change and contributing to the independence and security of Europe.

When you join us, you become part of a diverse, collaborative, and forward-thinking environment where your ideas and perspectives are valued. Our work extends beyond the lab—into field labs, innovation hubs, and partnerships with other faculties, research institutes, governments, and industry, both locally and globally.

We are committed to fostering an inclusive and welcoming workplace, assisted by an active Diversity & Inclusion team. This includes tangible support such as funding for extra personnel for family and caregiving responsibilities, mentoring programmes, and initiatives that promote cultural exchange and integration.

You don’t just join our faculty — you join a community where you can thrive, grow, and help shape the future of aerospace.

PhD Position on Spin Transport in Mesoscopic Devices with Two-dimensional Materials

Job description

We are looking for a highly motivated PhD candidate to join the Quantum Spintronics (QuSpin) Lab in the Department of Quantum Nanoscience, TU Delft. The project is aimed at developing spintronic devices based on graphene and van der Waals heterostructures. The project spans from building nano-constrictions and addressing spin-polarized quantum point contacts and quantum dots to functional topological spintronic devices based on quantum spin Hall currents. Your research will focus on understanding and controlling spin-polarized quantum transport at the nanoscale.

Your main tasks will include:

• Fabrication of graphene-based nanoelectronic devices using state-of-the-art cleanroom facilities
• Assembly of van der Waals heterostructures using dry-transfer techniques
• Low-temperature transport measurements
• Data analysis and modelling of quantum transport measurements
• Close collaboration with other researchers within the department, and interaction with leading experimental groups within the Dutch quantum ecosystem
• Attendence and presenting in national/international conferences

Research environment

You will join a young, ambitious, and interdisciplinary research group, led by Dr. Talieh Ghiasi, and embedded in the internationally renowned Kavli Institute of Nanoscience Delft. The QuSpin lab works at the interface of quantum materials, spintronics, and quantum transport, and offers a stimulating environment with strong experimental infrastructure, close collaborations, and excellent training opportunities. You will join a diverse and driven team of academic staff, PhD students and postdocs in Delft. Fostering an inspiring, friendly and supportive environment, we meet regularly to share ideas and knowledge or socialize. In addition, you will receive all the training you need to evolve as a scientist in this fast-developing field.

Job requirements
We are looking for a candidate who:

• Holds (or is close to obtaining) a Master’s degree in physics, applied physics, or nanoscience
• Has a strong interest in experimental condensed-matter physics and quantum nanoscience
• Is motivated to work hands-on in the cleanroom and cryogenic laboratories
• Is skilled at data analysis, problem-solving, and working in an international research team
• (highly recommended) Has prior experience with 2D material assembly and nanofabrication
• Has an advanced level in python programing (e.g. QCoDeS) needed for data acquisation and data analysis

TU Delft (Delft University of Technology)
Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.

At TU Delft we embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.

Challenge. Change. Impact!

Faculty Applied Sciences
With more than 1,100 employees, including 150 pioneering principal investigators, as well as a population of about 3,600 passionate students, the Faculty of Applied Sciences is an inspiring scientific ecosystem. Focusing on key enabling technologies, such as quantum- and nanotechnology, photonics, biotechnology, synthetic biology and materials for energy storage and conversion, our faculty aims to provide solutions to important problems of the 21st century. To that end, we educate innovative students in broad Bachelor's and specialist Master's programmes with a strong research component. Our scientists conduct ground-breaking fundamental and applied research in the fields of Life and Health Science & Technology, Nanoscience, Chemical Engineering, Radiation Science & Technology, and Engineering Physics. We are also training the next generation of high school teachers.

Click here to go to the website of the Faculty of Applied Sciences.