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全职 Research Fellow in AI and Multimedia Security (HG) 工作, 薪水, Singapore Institute Of Technology 公司招聘中 - Ricebowl

Research Fellow in AI and Multimedia Security (HG)

Singapore Institute Of Technology

Singapore

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  • Singapore Singapore

职位描述

岗位职责

Job Description
Research Fellow in AI and Multimedia Security (HG)
Posting Start Date: 09/10/2026
Schemes of Service: Research
Division: Networks & Cybersecurity
Employment Type: Fixed Term
About the Job
The Infocomm Technology (ICT) Cluster at the Singapore Institute of Technology invites applications for a Research Fellow on a 1-year contract under an AI Singapore-funded research programme on copyright protection for AI-assisted creative content. The programme develops technical safeguards that make visual content, including video and animation, verifiable, traceable, and revocable in AI-assisted production workflows.

Responsibilities:
1. Conduct research on robust watermarking for deep learning models and AI-generated visual content, including video and other temporal media.
2. Publish research findings in top-tier conferences and journals, and present research outcomes.
3. Maintain and update research documentation, code, and the Python programming platform.
4. Contribute to project deliverables and reports, and collaborate with project partners.
5. Assist with project-related student training.
6. Assist with project-related administrative matters.

Requirements:
1. A PhD in computer science, electrical engineering, or related fields.
2. Research experience in deep learning model watermarking, media watermarking, or AI security.
3. Proficient in Python machine learning libraries, especially PyTorch.
4. Strong technical paper writing skills, with submission and publication in the above areas.
5. Self-motivated, able to deliver results within a fixed timeframe, and good at communication and presentation.
6. Experience in video watermarking or watermarking for other temporal visual media will be an advantage.
7. Experience in model watermarking, watermark removal attacks, or model extraction will be an advantage.
8. Familiarity with machine unlearning or concept erasure in generative models will be an advantage.

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