Postdoctoral Researcher - Explainable AI for 3D Data (Spring, TX, US, 77389)

About us At ExxonMobil, our vision is to lead in energy innovations that advance modern living while reducing emissions. As one of the world’s largest publicly traded energy and chemical companies, we are powered by a unique and diverse workforce fueled by the pride in what we do and what we stand for. The success of our Upstream, Product Solutions and Low Carbon Solutions businesses is the result of the talent, curiosity and drive of our people. They bring solutions every day to optimize our strategy in energy, chemicals, lubricants and lower-emissions technologies. We invite you to bring your ideas to ExxonMobil to help create sustainable solutions that improve quality of life and meet society’s evolving needs. Learn more about our What and our Why and how we can work together . Why Join ExxonMobil? At ExxonMobil, we apply advanced optimization and machine learning techniques to solve some of the most challenging problems in energy, manufacturing, and low-carbon technologies. In this role, you will work on cutting-edge methods at the intersection of OR and AI, directly impacting critical business decisions and shaping next-generation computational decision-support capabilities. About the Role ExxonMobil is seeking a highly motivated Postdoctoral Researcher specializing in Explainable Artificial Intelligence (XAI) for large-scale 3D data analysis. The successful candidate will develop interpretable machine learning methods for segmentation, classification, and anomaly detection in high-dimensional volumetric datasets to support critical business and engineering decisions. This role is ideal for a recent Ph.D. graduate with expertise in XAI and deep learning applied to complex spatial data. The candidate will work closely with domain experts to create transparent, trustworthy AI systems that provide actionable insights for high-stakes applications. Key Responsibilities Key Responsibilities Develop explainable AI methods for deep learning models applied to 3D volumetric data. Design and implement models for segmentation, classification, and anomaly detection in large-scale datasets. Create techniques to improve model interpretability, transparency, and trustworthiness, including post hoc explanation and inherently interpretable approaches. Develop uncertainty-aware predictions to support decision-making in critical applications. Optimize models for scalability and performance on large 3D datasets. Evaluate models using both predictive accuracy and explainability metrics relevant to domain needs. Collaborate with domain experts to translate model outputs into decision-support tools. Implement workflows using modern ML frameworks and reproducible software practices. Communicate findings through technical reports, journal publications, and conference presentations. Example Research & Application Areas Explainable AI methods for deep neural networks 3D computer vision and volumetric data analysis Semantic and instance segmentation in large 3D volumes Anomaly detection in high-dimensional spatial data Interpretable representations for classification models Uncertainty quantification and confidence estimation in AI models Human-in-the-loop AI and decision-support systems Applications to subsurface imaging, industrial inspection, and sensor data Required Qualifications Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Computational Science, or a closely related field, with a focus on explainable AI or interpretable machine learning. Demonstrated research experience in explainable AI and deep learning, including one or more of: Model interpretability (e.g., saliency methods, attribution, feature importance) Explainability techniques for neural networks Interpretable model design Experience with 3D data (e.g., volumetric imaging, point clouds, or spatiotemporal data) and deep learning methods such as CNNs, transformers, or graph neural networks. Proven experience in segmentation, classification, or anomaly detection tasks. Strong programming skills in Python. Hands-on experience with machine learning frameworks such as PyTorch or TensorFlow. Strong analytical, problem-solving, and communication skills. Ability to work effectively in multidisciplinary teams. Preferred Qualifications Experience with XAI methods for computer vision or 3D data. Familiarity with uncertainty quantification, probabilistic ML, or Bayesian deep learning. Experience with large-scale data processing and GPU-accelerated training. Knowledge of evaluation metrics for explainability and model trustworthiness. Experience applying AI to engineering, geospatial, industrial, or scientific datasets. Strong publication record in XAI, machine learning, or computer vision. Demonstrated ability to translate research into decision-support applications. Desired Attributes Passion for developing trustworthy and interpretable AI systems. Interest in solving high-impact, real-world problems involving complex data. Ability to bridge machine le