Current Vacancies

Research Assistant (Generative AI and Sequence Modeling)

研究助理（生成式 AI 与时序建模方向）

岗位职责

1.负责医学视频数据的时序建模、动作表征及视频生成算法（如 Diffusion/DiT 架构）的研发。

2.参与大视觉语言模型（VLM）在特定医疗垂直领域的微调、部署以及多模态智能体（Agent）系统的搭建。

3.负责高质量数据清洗、时序流媒体处理、模型训练、调优及核心功能的工程闭环落地。

4.协助团队撰写高质量技术报告、专利申请书以及国际顶会/顶刊学术论文。

任职要求

1.专业背景：计算机、自动化、电子信息、生物医学工程、数学或相关专业，本科及以上学历（优秀的应届本科毕业生亦可）。

2.编程与框架：工程能力扎实，精通 Python 编程，熟练使用 PyTorch 深度学习框架，具备良好的代码规范和版本控制意识。

3.核心技能：深入理解深度学习与序列建模基础，在视频生成、时序序列建模（Transformers/RNNs）、大模型微调（SFT/PEFT）、或自监督学习（SSL）等方向至少有一项扎实的实战经验。

加分项

1.有大模型 Agent 框架（如 LangChain, LlamaIndex, AutoGPT）使用经验或 VLM 交互式开发经历者优先；

2.有视频流处理、动态视觉或时间序列预测等实际项目经验者优先；

3.具备良好的英文文献阅读与理解能力，能够快速复现前沿顶会（如 CVPR, NeurIPS, MICCAI）算法。

申请方式

请将个人简历发送至 hr02@cair-cas.org.hk，邮件主题请注明：应聘岗位－姓名－官网投递。

Job Responsibilities

Algorithm R&D: Responsible for the research and development of sequence modeling, action representation, and video generation algorithms (e.g., Diffusion/DiT architectures) for medical video data.

Agent System Development: Participate in the fine-tuning, deployment, and multimodal agent system construction of Large Vision-Language Models (VLMs) within specific medical domains.

Engineering Implementation: Execute the pipeline including data curation, real-time streaming data processing, model training, optimization, and engineering closure of core components.

Academic Output: Assist the team in drafting high-quality technical reports, patent applications, and research papers for top-tier international conferences/journals.

Job Requirements

Educational Background: Bachelor’s degree or higher in Computer Science, Automation, Electronic Information, Biomedical Engineering, Mathematics, or related fields. (Outstanding recent undergraduates are strongly encouraged to apply).

Programming & Frameworks: Solid engineering skills with proficiency in Python. Deep hands-on experience with PyTorch. Exceptional coding style and familiarity with version control are essential.

Core Competencies: Strong understanding of deep learning and sequential modeling fundamentals. Proven practical experience in at least one of the following domains: Video Generation, Sequence Modeling (Transformers/RNNs), Large Model Fine-tuning (SFT/PEFT), or Self-Supervised Learning (SSL).

Preferred Qualifications

Prior experience with LLM/Agent frameworks (e.g., LangChain, LlamaIndex, AutoGPT) or VLM interactive development is highly preferred.

Experience in video streaming processing, dynamic vision, or time-series forecasting projects is a plus.

Strong English literacy with a proven track rate of quickly reproducing algorithms from top-tier conference papers.

Application Method

Please send your resume to hr02@cair-cas.org.hk. For the email subject line, please indicate: Application for [RA - Generative AI and Sequence Modeling] - [Name] - [Applied via CAIR Official Website].