Postdoctoral Researcher - Explainable AI for 3D Data

ExxonMobil • Spring, TX

32d • Onsite

About The Position

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.

Requirements

• 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.

Nice To Haves

• 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.

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.

Benefits

• Relocation benefits may be available

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree