Postdoctoral Researcher (Summer 2027) – Applied Artificial Intelligence for Petroleum Engineering

The Houston Research Center's AIT Team is seeking a Postdoctoral Researcher to develop and deploy next-generation AI solutions for complex petroleum industry challenges. You will conduct applied research combining machine learning, physics-based modeling, and petroleum engineering, working with world-class researchers and domain experts on real-world upstream problems to create measurable business value for Saudi Aramco.

AI Research and Development

• →Develop machine learning, deep learning, and generative AI solutions for upstream oil and gas applications.
• →Investigate emerging methods such as LLMs, agentic AI, Physics-Informed Neural Networks, Fourier Neural Operators, Graph Neural Networks, foundation models, and reinforcement and Bayesian learning.
• →Design algorithms that integrate domain physics with data-driven methods.

Apply AI to reservoir characterization and modeling, production forecasting and optimization, drilling and geosteering, virtual flow metering, shale development, enhanced oil recovery and carbon management, and predictive maintenance.

• Build production-ready AI tools and workflows for Saudi Aramco operations, working with software engineers to move prototypes into enterprise applications.
• Use cloud, HPC, and GPU environments, and support AI integration into digital twin and decision-support systems.

• Strong knowledge of machine learning and statistical modeling.
• Strong Python and scientific computing skills, including ML frameworks such as PyTorch, TensorFlow, Scikit-Learn, and XGBoost.
• Experience handling large-scale scientific or engineering datasets.

• Experience developing AI solutions for oil and gas, with publications in AI, machine learning, petroleum engineering, or computational science.
• Knowledge of reservoir simulation software and workflows.
• Experience with HPC, GPU acceleration, and cloud computing; familiarity with MLOps and enterprise-scale AI deployment.
• Experience with digital twins, autonomous workflows, and intelligent agents.

• Ph.D. in Petroleum Engineering, Computer Science, Data Science, AI, Mechanical Engineering, Applied Mathematics, Geoscience, or a related field.

Experience in one or more of: petroleum engineering applications, physics-informed or scientific machine learning, reservoir simulation, geostatistics, computer vision, time-series forecasting, optimization and uncertainty quantification, and generative AI and foundation models.