At IBM work is more than a job – it’s a calling: To build. To design. To code. To consult. To invent. To collaborate.
Not just to do something better but to attempt things you’ve never thought possible.
If you are ready to help define the future of AI for Science and Decision Optimisation we invite you to join our team.
We are seeking a passionate and innovative Research Scientist to join our team advancing Foundation Models (FMs) for Mathematics and Optimization.
In this role you will conduct cutting-edge research at the intersection of AI dynamical systems and optimization developing next-generation models that can learn representations and optimal control of dynamical systems from limited context data.
You will explore and build new architectures for dynamical systems contributing to IBM’s mission to build foundational AI capabilities for decision optimization and industrial process management. Working closely with a multidisciplinary research team you will design prototype and publish novel methods that push the boundaries of AI for scientific and industrial applications.
- Strong research background in dynamical systems physics-inspired neural networks or neural operators for PDEs supported by a solid publication record.
- Proven experience in machine learning for control optimisation or physical process modelling.
- Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
- Solid understanding of mathematical modelling numerical methods and scientific computing.
- Strong problem-solving skills and the ability to work collaboratively in a research-driven environment.