Postdoc: Explainable AI for 3D Data

Exxon Mobil; Spring (TX); On-site; USD 65,000 - 90,000; Full time

Job summary

ExxonMobil invites applications for a Postdoctoral Researcher specializing in Explainable AI for large-scale 3D data analysis, based in Spring, Texas. The role focuses on developing interpretable machine learning methods for segmentation, classification, and anomaly detection in high-dimensional volumetric datasets to support critical engineering decisions.

The successful candidate will collaborate with domain experts, translate model outputs into decision-support tools, and contribute to

Qualifications

• Ph.D. in Computer Science, AI, ML, or a closely related field, with a focus on explainable AI.
• Demonstrated research experience in explainable AI and deep learning.
• Experience with 3D data (e.g., volumetric imaging, point clouds) and DL methods.
• Strong programming skills in Python.
• Hands-on experience with ML frameworks such as PyTorch or TensorFlow.
• Ability to translate model outputs into decision-support tools.
• Excellent communication and collaboration in multidisciplinary teams.

Responsibilities

• Develop explainable AI methods for 3D data analysis.
• Design and implement segmentation, classification, and anomaly detection models.
• Improve interpretability, transparency, and trustworthiness, including post hoc explanations.
• Develop uncertainty-aware predictions for critical decisions.
• Ensure scalability and performance on large 3D datasets.
• Collaborate with domain experts to translate outputs into decision-support tools.
• Communicate findings through technical reports and publications.
• Leverage ML frameworks and reproducible software practices.

Skills

Explainable AI

3D data analysis

Deep learning

Research experience

Communication skills

Education

Ph.D. in Computer Science/AI/ML or closely related field

Tools

PyTorch

TensorFlow

Python

Job description

ExxonMobil invites applications for a Postdoctoral Researcher specializing in Explainable AI for large-scale 3D data analysis, based in Spring, Texas. The role focuses on developing interpretable machine learning methods for segmentation, classification, and anomaly detection in high-dimensional volumetric datasets to support critical engineering decisions.

The successful candidate will collaborate with domain experts, translate model outputs into decision-support tools, and contribute to