Researcher Multiphysics AI

Job description

, IndiaJob Family Group: Research and DevelopmentWorker Type: RegularPosting Start Date: July 22, 2026Business Unit: Projects and TechnologyExperience Level: Experienced ProfessionalsJob Description: What’s the role? The Multiphysics AI & Product Innovation team develops and applies advanced Scientific Machine Learning, Computational Physics & Chemistry, and AI‑accelerated engineering methods to solve complex & high‑impact industrial challenges across Shell businesses. In this role, you will act as a Scientific Machine Learning specialist and Technical Integrator, developing and deploying physics‑guided AI solutions on high-value, complex Multiphysics problems. Your depth lies in AI algorithmic innovation complemented by a breadth of engineering judgment developed through close collaboration with domain experts and asset teams. Rather than being embedded as a single domain specialist, you will act as a technical integrator: understanding business needs well enough to select, adapt, and design the right scientific ML approaches for bespoke, high‑impact problems. You will engage with a diverse range of Shell businesses, including Low Carbon Fuels, Low Carbon Gas, CCS, and Upstream, working on problems such as Multiphysics asset behaviour and degradation (e.g., corrosion, electrochemical systems), Process and design optimization, model acceleration and decision support, operational monitoring and predictive insights for critical assets. Your value lies in understanding both the business problems worth solving as well as where, why & and under what operational constraints the Scientific ML algorithms work; and translating that understanding into robust, deployable solutions. This is a hands‑on experienced individual contributor role designed for someone who thrives at the intersection of deep science, engineering insight, and AI‑driven acceleration; with a strong commercial mindset and a passion for driving practical, scalable innovation. What you’ll be doing? • Design and develop Scientific ML and physics‑guided AI methods for Multiphysics and engineering applications, including Physics‑informed and physics‑constrained learning, Hybrid modelling (first‑principles solvers + data‑driven models), Reduced‑order models, neural surrogates, and emulators, Operator learning, graph‑based models, and uncertainty‑aware ML • Owning and guiding algorithmic decisions on when to apply PINNs, neural operators, surrogates, classical ML, or simulation‑centric approaches. • Work with asset/LOB domain experts to understand asset behaviour, operating constraints, uncertainties, and failure modes. Translate loosely defined engineering challenges into well‑posed Scientific ML problems.  • Ensure developed models are credible, validated against experimental, simulated, and operational data. Check for Robustness to sparse, biased, or imperfect measurements. Balance between accuracy, physical consistency, interpretability, and computational efficiency • Drive research and development in AI‑accelerated simulation, including Reduced‑order modelling and emulation. Performance optimization for large‑scale or time‑critical applications. • Act as a technical authority for Scientific ML within assigned Multiphysics initiatives and programs, contributing to shared standards and best practices.  • Model validation and credibility. Physics consistency and constraint handling • Lifecycle management of Scientific ML models • Stay current with advances in Scientific ML, AI, and computational science through external partners, including leading academic research groups and industry collaborators (e.g., cloud and AI platform providers) • Assess emerging methods for industrial relevance, adapting them into practical, defensible solutions. • Contribute to internal IP, publications, patents, and long‑term AI capability building. What we need from you? • PhD (or equivalent industry experience) in Applied Mathematics, Computational Physics, Computational Engineering, or AI/Machine Learning. • Deep hands‑on experience with Scientific ML techniques such as Physics‑informed and hybrid learning, Neural operators, surrogate modelling, or graph‑based ML, Uncertainty quantification, Bayesian inference, or probabilistic ML • Strong programming and prototyping skills (e.g., Python and modern ML frameworks). • Background in at least one major Multiphysics or engineering domain, such as Computational Fluid Dynamics (CFD), Structural or thermal analysis, Electrochemistry, Materials modelling or discovery • Demonstrated experience applying AI or Scientific ML to real, complex systems, not only synthetic benchmarks.  • Proven ability to work effectively with domain experts to navigate Incomplete physics, Data limitations, Operational and computational constraints • Experience in other asset‑intensive industries (e.g., aerospace, automotive, manufacturing, semiconductors) is also relevant. • Strong problem‑solving ability with a balance of independence and collaboration • Excellent communication skills, with the ability to explain complex technical topics to diverse audiences • Comfortable working in multidisciplinary, research‑to‑deployment environments • Experience in scientific computing platforms and cloud‑based workflows is desirable What we offer You bring your skills and experience to Shell and in return you work with talented, committed people on one of the most important challenges facing our planet. You’ll have the opportunity to develop the skills you need to grow in an environment where we value honesty, integrity, and respect for one ano