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Tier‑1 employer and metro location increase applicants, but niche PhD-level Scientific ML reduces overall applicant density.
Physics‑heavy scientific ML and asset domain expertise make skills less transferable across industries.
PhD requirement and mandatory scientific‑ML plus domain expertise create strict technical and educational filters.
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Develop and deploy physics-guided AI and Scientific Machine Learning solutions for complex Multiphysics engineering problems across diverse Shell businesses such as Low Carbon Fuels, CCS, and Upstream.
Translate loosely defined engineering challenges into well-posed Scientific ML problems by engaging deeply with domain experts to ensure model credibility, robustness, and alignment with operational constraints.
Act as the technical integrator and authority on Scientific ML methods within projects, responsible for algorithmic decisions, model lifecycle management, research, and development of scalable, industrially relevant AI-accelerated simulation technologies.
PhD or equivalent industry experience in Applied Mathematics, Computational Physics, Computational Engineering, or AI/Machine Learning.
Demonstrated deep hands-on experience with Scientific ML techniques such as Physics-informed learning, neural operators, surrogate modelling, or probabilistic ML.
Strong programming and prototyping skills in Python and modern ML frameworks.
Background and experience applying AI or Scientific ML in at least one major Multiphysics or engineering domain (e.g., CFD, electrochemistry). Work Experience Required: Not explicitly mentioned in the JD.
Experienced individual contributor skilled at integrating scientific AI with engineering domain knowledge to solve high-value, complex Multiphysics problems with operational impact.
Proven ability to collaborate effectively with domain experts, translating real industrial challenges into deployable AI solutions while balancing accuracy, interpretability, and efficiency.
Familiar with research-to-deployment environments, comfortable navigating incomplete physics, data limitations, and industrial operational constraints.