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Medium — strong Tier-1 brand and metro location increase competition; niche scientific ML reduces applicant density.
High — role demands domain-specific Scientific ML and engineering experience transferable only to similar industries.
High — PhD requirement and specialized Scientific ML expertise act as strict filters.
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Develop and deploy physics-guided Scientific Machine Learning (ML) solutions for complex Multiphysics engineering problems across diverse Shell business units.
Act as a technical integrator, selecting and adapting appropriate scientific ML methods to translate loosely defined engineering challenges into validated, deployable models.
Contribute to R&D in AI-accelerated simulation, lifecycle management of models, and establish best practices and standards for Scientific ML within Multiphysics initiatives.
PhD or equivalent industry experience in Applied Mathematics, Computational Physics, Computational Engineering, or AI/Machine Learning.
Hands-on experience with Scientific ML techniques including physics-informed learning, hybrid models, neural operators, surrogate modelling, uncertainty quantification, or probabilistic ML.
Strong programming skills in Python and modern ML frameworks; applied Scientific ML experience on real complex systems.
Background in at least one major Multiphysics or engineering domain such as Computational Fluid Dynamics, Structural/Thermal analysis, Electrochemistry, or Materials modelling.
Experienced contributor able to operate at the intersection of AI algorithmic development and engineering domain expertise, engaging effectively with diverse asset/domain experts.
Proven ability to deliver robust, validated Scientific ML models balancing interpretability, physical consistency, and computational efficiency on operational industrial problems.
Comfortable in multidisciplinary R&D to deployment environments with a strong commercial mindset and aptitude for integrating emerging scientific ML advances into scalable industrial solutions.