Postdoctoral Fellow – Generative AI Researcher – 3D Defect Synthesis | King Abdullah University of Science and Technology (KAUST)

Job Description

King Abdullah University of Science and Technology (KAUST) invites applications for a Postdoctoral Fellow position in Generative AI for 3D Defect Synthesis. 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.

The Challenge:
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.

Detection Scale Targets:

• cm (10⁻² m) — Structural cracks, delamination, component failure
• mm (10⁻³ m) — Voids, inclusions, solder bridges, swelling
• µm (10⁻⁶ m) — Micro-cracks, porosity, dendrite growth
• 100 nm (10⁻⁷ m) — Interface delamination, thin-film defects
• nm (10⁻⁹ m) — Grain boundary, lattice-level anomalies

What You Will Build:

• Defect synthesis engine: Diffusion, GAN, or NeRF-based models that insert controllable synthetic defects into 3D CT volumes
• Physics-aware rendering: Ensure generated defects respect X-ray attenuation physics for real sim-to-real transfer
• Multi-scale CT dataset pipeline: Tools to produce large-scale labelled synthetic training datasets
• Closed-loop detection training: Connect synthetic generation directly to YOLO/segmentation/anomaly detection training loops

Benefits:

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

Core Requirements:

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

Advantageous Background:

• Industrial NDT, materials science, semiconductor, or battery inspection experience
• Domain randomisation and sim-to-real transfer
• Multi-scale imaging (micro-CT, SEM, FIB-SEM, synchrotron data)
• HDF5/Zarr data schemas and GPU-accelerated volumetric processing
• Published work in generative models, synthetic data augmentation, or 3D reconstruction

Application Requirements (All mandatory):

Q1 — Debugging memory: Describe a specific bug or failure in a CT reconstruction or generative model pipeline that took more than a day to resolve.

Q2 — Constrained format: In exactly 3 bullet points, state the three hardest unsolved problems in synthetic-to-real transfer for CT-based defect detection.

Q3 — Literature opinion: Name one paper published after January 2024 that changed how you think about 3D generative models or volumetric defect synthesis.

Q4 — End-to-end system experience: Describe a generative AI system you built or contributed to that was used by an end user (not just a research prototype).

Q5 — Code evidence: Provide your GitHub/GitLab username and a link to one specific commit or pull request you are proud of.

Application Materials:

• CV / résumé (PDF, max 4 pages)
• Statement of purpose (1 page — why this role, why now)
• Answers to questions Q1–Q5
• GitHub / GitLab link for Q5
• Links to up to 3 relevant publications (optional)

Application Process:

Submit all materials in a single application via the link below. Email applications will not be reviewed. Applications reviewed on a rolling basis until position is filled.

Application Deadline: October 29, 2026

Location: King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia