Postdoctoral Researcher - Explainable AI for 3D Data
Core
Develop interpretable machine learning methods for segmentation, classification, and anomaly detection in high-dimensional volumetric datasets to support critical business and engineering decisions.
Role type
Postdoctoral Researcher (Explainable AI for 3D Data)
Builds
Transparent, trustworthy AI systems providing actionable insights for high-stakes applications in energy and low-carbon technologies.
Domain
Energy, Chemicals, Low-carbon technologies, 3D Computer Vision
Deliverable
production ML models
Required skills
Explainable AI (XAI), Deep Learning, 3D Data Analysis, Segmentation, Classification, Anomaly Detection, Python, PyTorch, TensorFlow, Uncertainty Quantification
Preferred skills
Probabilistic ML, Bayesian Deep Learning, GPU-accelerated training, Geospatial/Industrial dataset application
Technologies
PyTorch, TensorFlow, CNNs, Transformers, Graph Neural Networks
Responsibilities
Develop XAI methods for deep learning models on 3D volumetric data; Design models for segmentation, classification, and anomaly detection; Create techniques to improve model interpretability and trustworthiness; Develop uncertainty-aware predictions; Optimize models for scalability; Evaluate models using accuracy and explainability metrics; Collaborate with domain experts to translate outputs into decision-support tools; Implement reproducible software workflows; Communicate findings via reports and publications.
Seniority
Postdoctoral Researcher