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Open nowPosted 66 days ago

【实习】AI×有机合成研究-数据平台中心

aicarrier/522799209 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now【实习】AI×有机合成研究-数据平台中心aicarrier/522799 · Shanghai, Chinese Mainland

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  6. 57.8%60 days
This job: posted 66 days ago

The posting

上海人工智能创新中心正在构建基于 AI 的多步逆合成路径规划系统。团队已具备完整的 AI 建模与工程迭代能力，现需招募具备有机合成专业背景的在校研究生（硕士/博士），以实习生身份参与 AI 逆合成系统的路线评估与研发迭代工作。 你将运用自己的合成化学训练，对 AI 生成的多步合成路线进行专业评估与深度分析，并在此过程中为 AI 系统的持续改进提供来自化学视角的关键反馈。你的评估不只是"打分"——它将直接驱动模型的错误归因、评估标准的演进和系统能力的提升。这是一个将有机合成专业能力与前沿 AI 研究深度结合的实习机会。 （本岗位不涉及湿实验操作，不要求编程基础。）

核心工作内容 一、逆合成路线质量评估 - 阅读 AI 系统生成的多步合成路线（含每一步的反应类型、试剂条件、产物结构等） - 按项目评估规范，对整条路线与每一步反应进行系统化评估 - 标记不合理的反应步骤，简要说明问题所在（如条件不合理、选择性问题、官能团不兼容、区域化学或立体化学问题、起始原料可得性差等） - 给出可行性等级判断（理论上可行 / 需调整后可行 / 明显不可行） 路线评估是核心日常工作。你的评估结果将作为模型迭代的关键输入——帮助团队识别 AI 系统在哪些反应类型、官能团组合或合成策略上存在系统性偏差，并据此改进模型。 二、路线问题深度分析 在路线评估基础上，对评分较低、存在争议或需要进一步分析的路线，进行更深入的结构化分析： - 对指定路线逐步骤深度分析，标注关键问题步骤 - 对问题原因进行细化的化学解释（机理合理性、选择性、官能团兼容性、试剂/条件适配性、保护基策略、起始原料可得性等） - 对可改进的步骤，标注可能的修改方向或替代思路，并说明化学依据 - 按模板提交结构化分析结论 三、化学研发参与 - 协助团队将合成化学经验转化为可用于模型评估和系统迭代的规则与反馈 - 参与评估规范和打分体系的讨论与迭代完善 - 从化学专业角度为 AI 模型的评估指标设计提供输入 - 参与路线错误类型归纳，总结模型在不同反应类别中的典型失败模式

  • 在校硕士或博士研究生，有机化学相关方向（药物合成、全合成、有机方法学、工艺研发等）
  • 具备有机合成实际实验训练，在课题研究中参与过多步合成路线的设计或执行（非纯计算/理论方向）
  • 能够基于真实合成经验判断多步路线的整体可行性，而不仅是理解单步反应机理
  • 熟悉常见有机合成反应类型、保护基策略、选择性问题和官能团兼容性问题
  • 能够阅读英文合成文献，理解反应方案、条件筛选表和底物拓展表
  • 工作细致，对评估质量有责任感，能用简洁清晰的文字说明判断理由
  • 每周可投入 ≥ 3 天，能保持至少 3 个月的持续参与

优先录用 - 博士生优先，尤其是课题涉及多步全合成或药物分子合成者 - 有完整合成项目经验者优先（课题中完成过 5 步以上合成路线） - 有多步合成路线可行性评估、反应条件筛选、底物拓展或工艺优化经验者优先 - 了解逆合成分析的基本思路与策略 - 对 AI for Chemistry / CASP 方向有兴趣 - 有与计算或工程背景团队协作的经历

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