We seek candidate to drive the development of AI models in the context of Scientific Machine Learning. The focus is on innovating ways to bring physics-based knowledge into the improvement and understanding of statistical learning for real-world problems.
Job Responsibilities:
- Development of AI/ML models for the modelling of multi-scale, multi-physics problems for purposes such as design and scientific discovery
- Development of foundational methodologies for incorporating knowledge such as fundamental laws of physics into AI/ML models. This can be incorporated in different ways, including for uncertainty quantification, enabling effective learning with less data and meta-modelling
- Working with domain experts (e.g. in fluid dynamics) to better understand problems and acquire context for the advancement of physics-informed learning
- Collaboration with other partners such as other research institutes, academic partners and other relevant stakeholders
Requirements:
- PhD in Computer Science, Engineering or other relevant disciplines
- Having some background in engineering (i.e., fluid dynamics), numerical methods (e.g. finite volume or finite element methods) and/or statistical learning (i.e., deep learning)
- Strong skills in multi-language programming (i.e., Python) and can work independently in modeling, algorithm design and coding implementation
- Experience in customizing open-source programs
- Good interpersonal and communications skills
- Ability to work effectively as part of a small, agile team, resourceful and self-driven
- Good command of written and spoken communication skill