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Open nowPosted 43 days ago

数据工程科研实习生

sii-czxy/campus170 open roles

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Your applicationOpen now数据工程科研实习生sii-czxy/campus

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Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 57.8%60 days
This job: posted 43 days ago

The posting

1. 阅读并整理 AI for Biomedical Science、生物医学数据工程、免疫信息学、多模态数据建模等方向的前沿论文和技术资料，了解人工智能驱动生物医药研发的数据基础建设方法。 2. 参与免疫数据库建设相关工作，包括抗原表位信息抽取、序列数据整理、数据清洗、质量控制和标准化处理，支撑大规模免疫数据体系构建。 3. 开展生物序列数据分析工作，包括序列聚类分析、同源匹配验证、数据一致性检查等，提升数据库数据质量和可用性。 4. 协助搭建生物医学文献分析 Agent 工作流，参与多智能体协作流程调试，包括工具调用、长程记忆、知识检索和自动化报告生成等模块。 5. 参与 Benchmark 评测体系建设，根据项目需求协助开展实验设计、评测任务构建、金标准制定以及模型结果分析。 6. 参加课题组组会并进行阶段性研究汇报，整理实验记录、技术文档和数据处理流程。

1. 招募对象：本科生、硕士研究生。 2. 计算机、人工智能、数学、统计、生物信息学、生物医学等相关背景优先，对生物医药研究感兴趣者优先。 3. 具备 Python 编程基础，了解机器学习、深度学习或数据分析基本方法，能够完成数据处理、脚本开发和实验分析。 4. 对以下方向具有研究兴趣：AI for Biomedical Science；生物医学数据工程；免疫信息学与大规模数据库构建；科学智能体与自动化科研流程；生物医学知识挖掘。 5. 能够阅读英文技术资料和科研论文，具备数据整理、实验设计和结果分析能力。 6. 具备以下经历者优先：生物信息学、生物医学数据分析项目经验；文献挖掘、知识抽取、数据标注或数据库建设经验；大语言模型、Agent、RAG 系统开发经验；Python 数据处理、机器学习项目或开源项目经历。 7. 具备较强的数据敏感性和问题分析能力，能够参与大规模生物医学数据处理、质量控制和智能化分析流程建设。 8. 对“如何利用人工智能构建高质量生物医学知识体系，支撑药物研发与生命科学发现”这一方向具有持续兴趣，能够稳定投入科研时间。 9. 实习安排：线上线下结合；周期6个月以上；每周投入10–20小时。

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