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Strong employer brand but niche scientific ML role reduces broad applicant competition.
Role requires deep Scientific ML and engineering domain knowledge, limiting cross-industry transferability.
PhD requirement and specialised scientific ML plus domain expertise create strict hiring filters.
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Develop and deploy physics-guided AI and Scientific ML solutions to solve complex multiphysics problems across various Shell business domains such as Low Carbon Fuels, CCS, and Upstream.
Translate engineering challenges into well-posed Scientific ML problems, ensuring model credibility, robustness, and operational relevance against real-world data and constraints.
Act as technical authority and integrator for Scientific ML methods, guiding algorithmic choices, driving algorithmic innovation, validation, lifecycle management, and adopting emerging methods for industrial application.
PhD or equivalent experience in Applied Mathematics, Computational Physics, Computational Engineering, or AI/Machine Learning.
Deep hands-on experience in Scientific ML techniques including physics-informed learning, hybrid modeling, neural operators, surrogate modeling, uncertainty quantification, or probabilistic ML.
Strong programming and prototyping skills in Python and modern ML frameworks.
Work Experience Required: Demonstrated experience applying AI or Scientific ML to complex, real industrial systems; other asset-intensive industry experience also relevant.
Operates effectively at the intersection of deep AI algorithmic innovation and engineering domain expertise, serving as a technical integrator rather than a single-domain specialist.
Experienced in multidisciplinary, research-to-deployment environments, balancing scientific rigor, commercial impact, and operational constraints in asset-intensive industries.
Capable of navigating incomplete physics, data limitations, and engineering uncertainties by collaborating closely with domain experts and shaping practical, scalable AI-driven solutions.