Overview
UAEU's Computer and Network Engineering Department is recruiting a postdoctoral fellow for advanced AI research spanning deep learning, vision-language models, large language models and multimodal systems.
Complete research opportunity details
The role focuses on next-generation intelligent systems for visual understanding, reasoning, automated report generation, decision support and adaptive learning from multimodal data. Work includes transformer-based models, computer vision, generative AI, reinforcement learning, evolutionary optimization, benchmarking, explainability, research publication and deployment-oriented prototypes. UAEU states a salary range of AED 10,000–11,000 per month and an official close date of September 30, 2026.
Eligibility requirements
Applicants need a PhD in Computer Engineering, Computer Science, Data Science, Electrical Engineering or a related field. Required strengths include deep learning/neural networks, transformer architectures, Python, PyTorch or TensorFlow, computer vision and multimodal AI. Publications in strong AI venues and familiarity with VLM/LLM systems are preferred.
Tips for this postdoctoral opportunity
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- Match your research background to the project, laboratory, supervisor and required methods rather than applying only from the title.
- Prepare a focused academic CV, transcripts, publications and research statement or proposal when requested by the official call.
- Confirm funding duration, stipend/salary, tuition coverage, start date and eligibility for international applicants.
- Contact a supervisor before applying only when the official instructions recommend or require it.
Verification notes
Verified 2026-08-29 against UAEU careers PostingDetails/5261. The page currently exposes Apply, states AED 10,000–11,000 per month, and lists September 30, 2026 as the close date. No closing clock time/time zone is stated, so deadline is null.
Job and Scholarship is the discovery and verification layer. Confirm eligibility, dates, salary/funding and application instructions on the original source before submitting anything.
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