We are seeking a highly motivated to contribute to the next generation of reservoir modeling technologies. This role focuses on the of advanced computational methods combining . You will work on developing novel algorithms, enhancing simulation capabilities, and bridging data-driven and physics-based modeling approaches to support the energy transition and improve reservoir management workflows. * Conduct in reservoir simulation, computational physics, and data-driven methods. * Develop and prototype novel algorithms that integrate , including surrogate modeling, reduced-order modeling, and hybrid physics-ML models. * Research and implement advanced , including ensemble-based methods, adjoint-based gradient optimization, and Bayesian inference for history matching and uncertainty quantification. * Develop and apply for field development planning, production enhancement, and reservoir control under uncertainty. * Collaborate with cross-disciplinary teams including reservoir engineers, geoscientists, data scientists, and software engineers. * Publish research outcomes in peer-reviewed journals, patents, and present at industry and academic conferences. * Provide technical leadership in framing R&D roadmaps, identifying high-impact research directions, and supporting technology transfer into commercial or operational tools. * Contribute to the development of internal software prototypes or production-grade software for reservoir modeling and AI-enabled workflows. * Ph.D. in with a focus on numerical simulation, optimization, or machine learning applications. * Strong background in , linear and nonlinear solvers, and reservoir flow physics. * Expertise in , including finite difference, finite volume, or finite element methods applied to multiphase subsurface flow. * Demonstrated research experience in one or more of the following: + (e.g., surrogate modeling, neural networks, Gaussian processes, physics-informed ML) + (e.g., Ensemble Kalman Filter, Ensemble Smoother, Adjoint-based optimization, Bayesian inference) + (e.g., field development planning, well control optimization, robust optimization under uncertainty) * Proficiency in (ideally Python and MATLAB) for algorithm development and prototyping. * Proven track record of in relevant technical domains. * Experience integrating , including Physics-Informed Neural Networks (PINNs) or hybrid models. * Knowledge of high-performance computing (HPC), parallel programming, or cloud computing for large-scale simulations. * Familiarity with open-source or commercial reservoir simulators (e.g., MRST, Open Porous Media, Eclipse, Intersect, tNavigator, CMG). * Experience with probabilistic modeling, uncertainty quantification, and decision-making under uncertainty. * Background in related domains such as is a plus. * Strong analytical and problem-solving skills with a rigorous scientific approach. * Ability to communicate complex technical ideas clearly to both technical and non-technical audiences. * Self-driven, collaborative, and passionate about advancing the state of the art in reservoir engineering and computational sciences. * Comfortable working in both independent research settings and collaborative, multi-disciplinary environments. * Work on cutting-edge problems at the intersection of . * Be part of a collaborative R&D team influencing the future of energy, carbon management, and sustainable subsurface technologies. * Opportunities to publish, patent, and contribute to open-source software or commercial products. * Competitive compensation, research freedom, and professional growth in a dynamic, innovation-driven environment.