Current Vacancies

Intern-Embodied Continual Learning and Multimodal Large Language Models

实习生-具身持续学习与多模态大语言模型

Job Responsibilities

1. Participate in research on continual learning algorithms for medical scenarios, assisting in addressing long-tail and forgetting problems.

2. Assist in continual fine-tuning and reinforcement fine-tuning methods for MLLMs, including model architecture improvements and training strategy experiments.

3. Conduct experimental analysis and data organization under team guidance.

4. Support the drafting of technical documentation, research papers, or patent materials.

Requirements

1. Current undergraduate or graduate students in Computer Science, Artificial Intelligence, Mathematics, or related fields.

2. Strong interest in AI research with foundational literature review and learning abilities.

3. Solid logical thinking and teamwork skills, capable of completing tasks on schedule.

4. Proficient in Python and PyTorch，prior experience in deep learning projects is preferred.

5. Familiarity with LLM, MLLM (e.g., LLaVA, Qwen-VL), MoE, reinforcement learning is a plus.

Application Method

Please submit CV to hr02@cair-cas.org.hk. The subject of the email should be marked as Application for [Intern- Embodied Continual Learning and Multimodal Large Language Models]-[Name].

岗位职责

1. 参与面向实际医疗场景的持续学习算法研究，协助解决数据长尾分布和模型遗忘问题。

2. 协助多模态大模型的微调与优化，包括模型结构改进、训练策略实验等。

3. 在团队指导下完成实验分析、数据整理。

4. 参与技术文档、论文或专利材料的撰写辅助工作。

职位要求

1. 计算机、人工智能、数学等相关专业在读本科生或研究生。

2. 对AI科研有兴趣，具备基础文献阅读和学习能力；

3. 良好的逻辑思维和团队协作意识，能按时完成任务。

4. 熟悉Python和PyTorch，有深度学习项目经验者优先；

5. 了解LLM, MLLM (如LLaVA, Qwen-VL)，MoE、强化学习等基础知识者优先。

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[实习生-具身持续学习与多模态大语言模型]-[姓名]。