The exponential increase of computational power over the last decade has enabled scale-resolving simulations (SRS) of turbulent flows at an unprecedented resolution. In combination with high-performance computing (HPC), parallel computational fluid dynamic simulations can currently resolve spatial and temporal scales of industrially-relevant turbulent flows within days/weeks. On the other hand, typical design cycles in industry still make extensive use of low-fidelity simulations which can provide fast but inaccurate solutions depending on the flow complexity.
To close this gap, this PhD will explore machine-learning (ML) methods to significantly reduce the turnaround time of SRS, thus enabling their use for industrial design processes. By combining state-of-the-art numerical methods and data-driven modelling techniques, the PhD candidate will investigate novel frameworks to accelerate SRS in HPC environments.
The PhD candidate will work at the Ship Hydrodynamics section of the Department of Maritime and Transport Technology (Faculty of Mechanical Engineering). This environment facilitates the exchange of research knowledge with colleagues also working in CFD and ML, both in applied and fundamental areas. The candidate will develop transferable skills such as HPC software development, extensive physical understanding of turbulent flows, and experience in modern ML architectures such as neural operators and transformers.
Job requirements- MSc in Mechanical/Maritime/Aeronautical Engineering, Physics, Applied Mathematics, or related discipline.
- Proven track record in numerical methods and computational fluid dynamics.
- Proven track record in machine-learning methods for computational physics.
- Proven programming skills in (preferably) Fortran or Julia, or C/C++ and Python. Where applicable, a link to your open source repositories is recommended.
- Experience in high-performance computing using MPI.
- Experience in GPU programming using OpenACC, CUDA, CUDA-Fortran, Julia, or related tools.
- Experience in CFD meshing software.
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Faculty Mechanical EngineeringFrom chip to ship. From machine to human being. From idea to solution. Driven by a deep-rooted desire to understand our environment and discover its underlying mechanisms, research and education at the ME faculty focusses on fundamental understanding, design, production including application and product improvement, materials, processes and (mechanical) systems.
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Conditions of employmentDoctoral 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.
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Additional informationAre you interested in this vacancy? Please apply no later than 1 December 2025 via the application button and upload the following documents:
- CV including the contact details of 2 referees
- Cover (motivation) letter
- The abstract of your MSc thesis (in English)
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.
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