Postdoctoral Fellow - Generative AI Researcher - 3D Defect Synthesis at Karlstad University

المنصب	Postdoctoral Fellow - Generative AI Researcher - 3D Defect Synthesis
نُشر في	16 Aug 2026
انتهت الصلاحية	15 Sep 2026
الشركة	Karlstad University
الموقع	Makkah \| SA
نوع الوظيفة	Full Time

الوصف الوظيفي:

أحدث معلومات الوظائف من Karlstad University لمنصب Postdoctoral Fellow - Generative AI Researcher - 3D Defect Synthesis. If the Postdoctoral Fellow - Generative AI Researcher - 3D Defect Synthesis الشاغرة في Makkah تتوافق مع مؤهلاتك، يرجى تقديم أحدث طلب أو سيرة ذاتية مباشرة من خلال بوابة وظائف Jobkos المحدثة.

يرجى ملاحظة أن التقديم على وظيفة قد لا يكون سهلاً دائماً، حيث يجب على المرشحين الجدد استيفاء مؤهلات ومتطلبات معينة تحددها الشركة. نأمل أن تكون الفرصة المهنية في Karlstad University لمنصب Postdoctoral Fellow - Generative AI Researcher - 3D Defect Synthesis أدناه تتوافق مع مؤهلاتك.

Postdoctoral Fellow - Generative AI Researcher - 3D Defect Synthesis

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

الموقع

King Abdullah University of Science and Technology (KAUST)

Open Date

Apr 29, 2026

Deadline

Oct 29, 2026 at 11:59 PM Eastern Time

Description

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 التالي 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.

100 nm (10 m) - Interface delamination, thin-film defects

What You Will Build

Defect synthesis engine: Diffusion, GAN, or NeRF-based models that insert controllable synthetic defects into 3D CT volumes, parameterised by type, size, الموقع, 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.
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.

Application Questions (Required)

All five questions below are mandatory. Please answer them in your own words. Generic or AI-generated responses will not advance in the process.

Q1 - Debugging memory

Describe a specific bug or failure in a CT reconstruction or generative model pipeline that took you more than a day to resolve. What was the root cause, and what did you change? (We are looking for a real incident, not a hypothetical.)

Q2 - Constrained format

In exactly 3 bullet points - no more, no less - state what you believe are the three hardest unsolved problems in synthetic-to-real transfer for CT-based defect detection. Answers with more or fewer bullets will be disqualified.

Q3 - Literature opinion

Name one paper published after January 2024 that changed how you think about 3D generative models or volumetric defect synthesis. Give the title, one thing it got right, and one thing you would push back on. Include the DOI or arXiv ID.

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). What was the input, what did the model produce, and how did you handle the gap between model output quality and what a real user actually needed? We are looking for evidence of practical deployment thinking, not just model training experience.

Provide your GitHub or GitLab username and a link to one specific commit or pull request you are proud of. In 2 sentences, explain the non-obvious decision you made there. Repositories must contain real commits predating this posting.

Applications are reviewed on a rolling basis. المنصب open until filled.

Job details

Title

Postdoctoral Fellow - Generative AI Researcher - 3D Defect Synthesis

KAUST is devoted to finding solutions for some of the world's most pressing scientific challenges in the areas of food, water, energy and the envir

معلومات الوظيفة:

الشركة: Karlstad University
المنصب: Postdoctoral Fellow - Generative AI Researcher - 3D Defect Synthesis
مكان العمل: Makkah
الدولة: SA

كيفية تقديم الطلب:

بعد قراءة وفهم المعايير ومتطلبات الحد الأدنى من المؤهلات الموضحة في معلومات الوظيفة Postdoctoral Fellow - Generative AI Researcher - 3D Defect Synthesis at the office Makkah أعلاه، أكمل فوراً ملفات طلب الوظيفة مثل خطاب التقديم، السيرة الذاتية، نسخة من الشهادة الجامعية، كشف الدرجات، والملاحق الأخرى كما هو موضح أعلاه. أرسلها عبر رابط الصفحة التالية أدناه.

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