Postdoctoral Fellow - Generative AI Researcher - 3D Defect Synthesis

King Abdullah University of Science and Technology: Postdoc Positions: Physical Science and Engineering Division (postdoc): Material Science and Engineering (postdoc)

Location

King Abdullah University of Science and Technology (KAUST)

Open Date

Apr 29, 2026

Deadline

Oct 29, 2026 at 11:59 PM Eastern Time

Description

Real defects in batteries, consumer electronics, and structural components are rare, expensive to induce, and nearly impossible to reproduce at scale. We are building the next generation of AI-powered industrial inspection — and the bottleneck is training data.

The mission of this role is to develop generative AI models that synthesize geometrically and physically plausible defects directly into 3D CT and X-ray volumetric data — spanning the full physical scale from centimeter-level structural failures down to nanometre-level material anomalies — creating the synthetic datasets needed to train high-fidelity detection models without requiring real defective samples.

• b 100 nm (10-7 m) — Interface delamination, thin-film defects

Responsibilities

• Defect synthesis engine: Diffusion, GAN, or NeRF-based models that insert controllable synthetic defects into 3D CT volumes, parameterised by type, size, location, and severity.
• Physics-aware rendering: Ensure generated defects respect X-ray attenuation physics, Hounsfield unit gradients, CT reconstruction artefacts, and material contrast — enabling real sim-to-real transfer.
• Multi-scale CT dataset pipeline: Tools to produce large-scale labelled synthetic training datasets from micro-CT, CBCT, and X-ray projections at multiple resolutions and across device types.
• Closed-loop detection training: Connect synthetic generation directly to YOLO / segmentation / anomaly detection training loops, with real-scan validation benchmarks and iterative refinement.

Benefits

• Internationally competitive, tax-free salary
• On-campus housing included
• Annual travel allowance
• Access to world-class CT, micro-CT, and imaging facilities on campus
• Vibrant international research community (100+ nationalities)

Qualifications

Core Requirements

• PhD in Computer Vision, Medical Imaging, Applied Machine Learning, or a closely related field.
• Hands-on experience with generative models — diffusion (DDPM/LDM), GANs, VAEs, or Neural Radiance Fields applied to 3D or volumetric data.
• Strong background in 3D CT or X-ray imaging: reconstruction pipelines, projection physics, or volumetric segmentation.
• Proficiency in Python and PyTorch. Familiarity with MONAI, ASTRA toolbox, or SIRT/FDK reconstruction is a strong advantage.

Advantageous Background

• Industrial NDT, materials science, semiconductor, or battery inspection experience.
• Domain randomisation and sim-to-real transfer for detector training.
• Multi-scale imaging: micro-CT, SEM, FIB-SEM, or synchrotron data handling.
• HDF5 / Zarr data schemas and GPU-accelerated volumetric processing.