Internship, Master's Thesis, Semester Project
Zurich
Key information
- Publication date:17 September 2025
- Workload:100%
- Place of work:Zurich
Internship, Master's Thesis, Semester Project
AI for Physics & Engineering Simulations
Ref. 2025_023
We are launching an exciting new research initiative at IBM Research to leverage AI for developing novel algorithms that accelerate computer simulations of complex physical and engineering systems.
Many natural and engineered phenomena are governed by complex differential equations. Traditional solvers require significant domain expertise, and can only handle these problems on the world’s largest supercomputers—expensive resources typically reserved for a small number of applications. Even then, a single simulation may take weeks to complete, and its results cannot be directly reused.
AI-driven methods are changing this paradigm. By learning the underlying physics from simulation data, AI can build surrogate models that deliver high-fidelity results in seconds, enabling faster innovation across science and engineering.
Our team has already demonstrated this potential: we successfully developed a neural surrogate model to solve a highly challenging fluid dynamics problem for a major race car manufacturer, achieving remarkable accuracy on complex geometries featuring wings, winglets, and other aerodynamic appendices. Given solely the geometry of the vehicle, the full fluid flow and pressure field over the car can be predicted – allowing engineers to design improvements at a significantly faster rate.
As a student in our team, you will have the opportunity to contribute to this cutting-edge research, tackling problems that span:
- Fluid dynamics
- Solid mechanics
- Fluid-Structure Interaction
- Heat transfer
- Friction and contact mechanics
- Chemistry and materials science
- Coupled multi-physics systems
- ... and many others
We now plenty of ideas on how to bring this work to the next level, and we can offer research and innovation projects on topic such as:
- Algorithm development
- Scalability, parallelization, and computational efficiency
- Geometry and mesh optimization
- Foundation models for ODEs and PDEs
- Inverse modeling
Why Join Us?
A unique aspect of our projects is the chance to work with real-world, client-provided datasets that are not publicly available. These datasets present challenges that often push beyond the limits of today’s most advanced AI methods, making them an excellent opportunity for impactful innovation.
As part of our team, you will collaborate closely with experienced Research Scientists and AI Software Engineers who will guide you through ambitious research tasks and help you succeed. Also, you will have the opportunity to work with our academic and industrial partners. In addition, you will have access to modern GPUs and cloud infrastructure to accelerate your work.
We actively encourage interns to file their first patent with us and to publish in top AI conferences — giving you a strong foundation for both academic and industry careers.
Minimum Qualifications
- Bachelor’s degree in Computer Science, Machine Learning, or a related technical field (or equivalent practical experience)
- Strong programming skills
- Proficiency with Unix/Linux environments
- Excellent communication and presentation skills in English
- Team-oriented, self-motivated, and able to solve problems independently
Preferred Qualifications
- Experience with one or more of the following:
- Physics-Informed Neural Networks (PINNs)
- Neural Operators / Neural Solvers
- Algorithm development
- Distributed computing, data structures, test automation, or CI/CD - Hands-on experience with PyTorch
- Advanced programming skills (e.g., C/C++ or CUDA)
- Ability to work independently and adapt in a fast-paced, evolving research environment
References & Further Reading
- IBM Research is turbocharging algorithm development for a world with quantum computing and AI
- Neural operators as a PDE solution paradigm: https://arxiv.org/abs/2108.08481
- Breakthrough approach in the car domain: https://arxiv.org/abs/2309.00583
- Recent SOTA work on Neural Operators: Transolver GitHub and Arxiv article
Diversity
IBM is committed to diversity at the workplace. With us you will find an open, multicultural environment. Excellent flexible working arrangements enable all genders to strike the desired balance between their professional development and their personal lives.
How to apply
Please submit your CV including contact information for two or three references. We encourage candidates to also share a 3-minute video, in which they introduce themselves, as well as highlight their motivation and expertise. The video is not mandatory.
Interview process
After the initial screening based on the uploaded documentation, identified candidates will be contacted for a first technical discussion on their experience, background, and motivations, followed by a coding interview and a project matching discussion.