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Open nowPosted 43 days ago

基于 LLM 的数据标注与标准化实习生

sii-czxy/campus170 open roles

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Your applicationOpen now基于 LLM 的数据标注与标准化实习生sii-czxy/campus

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This job: posted 43 days ago

The posting

1. 设计并维护化学数据标注规范，针对化学反应、催化体系、谱学表征和实验数据等不同类型信息，制定结构化数据 schema、标注规则和 Few-shot 标注示例，保证多来源数据的一致性与可用性。 2. 搭建 LLM 辅助的数据标注与信息抽取流程，利用 GPT、Claude、Qwen 等大语言模型对科研文献、实验记录和计算结果进行自动化解析，抽取反应物、产物、实验条件、催化性能等关键化学信息。 3. 编写数据处理脚本，实现大规模文献和实验数据的批量解析、结果整理与格式转换，提升化学数据构建效率。 4. 建立 LLM 输出质量控制体系，包括规则校验、模型辅助检测和人工复核流程，分析信息抽取准确率和错误类型，持续优化 Prompt、标注规范和校验策略。 5. 协助构建原子级催化剂数据集，对抽取结果进行清洗、去重、标准化处理，并整合至“组分—结构—谱学—活性”多维化学数据库体系。 6. 参与数据集建设、实验流程优化和项目组讨论，定期汇报阶段性工作进展，根据项目需求承担新的数据整理与智能化标注任务。

1. 招募对象：本科生、硕士研究生。 2. 化学、化工、材料、计算机、人工智能、数据科学、信息管理等相关专业背景优先；化学背景不是必需，但需具备学习化学数据规范和科研知识的兴趣。 3. 具备 Python 编程能力，熟悉 pandas 等数据处理工具，能够完成数据清洗、格式转换和批量处理任务。 4. 了解大语言模型基本使用方法，具备以下经验者优先：使用 GPT、Claude、Qwen 等大模型 API；Prompt Engineering；LLM 信息抽取或自动化工作流搭建。 5. 能够阅读英文科研论文和技术文档，理解化学领域文献中的数据结构和实验信息。 6. 具备以下经历者优先：数据处理、数据库建设或知识图谱项目；爬虫、文本挖掘、自然语言处理（NLP）相关项目；AI 工具辅助科研或自动化数据处理经验。 7. 本科生要求：具备基础编程能力和较强学习能力；不要求已有正式科研经历，更关注数据规范意识、责任心和对 AI+化学交叉方向的兴趣。 8. 硕士研究生要求：具备一定数据分析、机器学习或科研项目经验；能够参与数据流程设计、质量分析和方法优化工作。 9. 工作认真细致，对数据质量敏感，能够接受大量重复性但规则明确的数据标注、校验和复核任务。 10. 对 AI for Science、大模型应用、化学数据智能化和科学基础模型方向具有持续兴趣，能够稳定投入科研时间。 11. 实习安排：线上线下结合；周期3–6个月；每周投入10–20小时。

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