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Open nowPosted 35 days ago

多模态大模型科研实习生

sii-czxy/campus170 open roles

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Your applicationOpen now多模态大模型科研实习生sii-czxy/campus

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  4. 19.3%14 days
  5. 35.6%30 days
  6. 58.1%60 days
This job: posted 35 days ago

The posting

1. 阅读并整理多模态大模型架构、训练方法、视觉—语言模型（VLM）、多模态理解与生成以及模型能力评测等方向的前沿论文，梳理代表性方法、技术路线和研究进展。 2. 参与多模态大模型开源模型复现工作，熟悉模型结构、数据处理流程、训练与推理流程，在指导下完成基础实验和模型性能验证。 3. 参与图像、文本等多模态数据集的整理与处理工作，包括数据筛选、格式转换、质量检查和评测样本构建，支持模型训练和能力评估。 4. 编写或完善多模态模型评测脚本，开展模型理解能力、生成能力、推理能力等方面的实验评估，分析不同模型方法的性能差异。 5. 在指导下开展不同模型架构、训练策略和数据配置条件下的对比实验，研究模型效果、计算效率以及规模扩展能力之间的关系。 6. 记录实验过程、整理实验结果和技术文档，参与课题组讨论并进行阶段性汇报，根据实验发现协助开展后续模型优化和研究探索。

1. 招募对象：本科生。 2. 计算机、人工智能、电子信息、数学等相关专业背景优先。 3. 具备机器学习、深度学习基础知识，了解神经网络、Transformer、多模态学习等基本概念，对多模态大模型研究方向具有兴趣。 4. 具备 Python 编程基础，能够完成数据处理、实验脚本编写和结果分析；了解 PyTorch、Linux 等深度学习开发工具者优先。 5. 能够阅读英文论文和技术资料，具备基础论文复现、模型调试和实验分析能力。 6. 不要求已有正式科研经历，更关注学习能力和研究兴趣；具有课程项目、论文复现、算法竞赛、开源项目或大模型应用实践经历者优先。 7. 具备良好的实验记录习惯和沟通能力，能够认真整理实验数据、分析实验现象，并主动学习相关技术。 8. 对多模态智能、大模型训练、模型架构优化和人工智能前沿技术具有持续兴趣，能够稳定投入科研时间。 9. 实习安排：线下科研实习；实习周期：3–6个月；每周投入时间：20–30小时。

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