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Open nowPosted 63 days ago

RNA 干湿闭环实验科学家

c0fu1j3a9m0/referral33 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen nowRNA 干湿闭环实验科学家c0fu1j3a9m0/referral · Shanghai, Chinese Mainland

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market. c0fu1j3a9m0/referral postings stay open a median of 1 days.

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 63 days ago

c0fu1j3a9m0/referral median: 1 days open

The posting

1、RNA 药物实验体系建设：建立并优化 RNA 药物相关实验体系，包括但不限于： 1）siRNA / ASO 的细胞活性验证； 2）RNA 转染与递送实验； 3）qPCR / RT-qPCR、ELISA、荧光及多功能酶标等检测； 4）RNA 剂量—效应关系、时间梯度及细胞毒性实验； 5）RNA 序列、化学修饰及递送体系的功能评价。 根据项目需求建立稳定、可重复、适合规模化实验的标准实验流程和 SOP。 2、干湿闭环研发 1）与 AI / 算法团队共同设计实验，使实验数据能够直接用于模型训练、验证和迭代。 2）将计算模型提出的 RNA 序列、化学修饰、递送方案等设计转化为实验验证方案。 3）根据实验结果帮助模型团队分析：活性；剂量响应；细胞毒性；递送效率；off-target；sequence–activity relationship 等关键指标。 4）参与建立 Design → Experiment → Data → Model → New Design 的自动化研发闭环。 3、参与建设自动化实验平台 1）与工程团队一起配合，共同完成湿实验流程自动化。 2）参与实验 protocol 的参数化、标准化和数字化，使实验能够被实验调度系统和 AI Agent 调用。 3）建立实验数据质量控制体系，提高实验重复性和跨批次一致性。 4、高通量实验与数据生成 根据 AI 模型需求设计高信息密度实验，而不仅仅完成单个候选物验证。 参与建立 RNA sequence / modification / delivery 等维度的大规模实验数据集。 探索 combinatorial experiment、active learning、DoE 等实验设计方法，提高单位实验产生的数据价值。 5.、CRISPR及未来技术拓展 1）随着平台发展，参与建立 CRISPR / 基因编辑相关实验能力，包括但不限于：逐步将平台从 RNA therapeutics 扩展为面向 RNA engineering + gene perturbation + functional genomics 的通用自动化实验平台。

1、生物学、生物化学、药学、分子生物学、生物工程或相关专业博士学历。 2、扎实的分子生物学和细胞实验基础。 3、熟练掌握至少部分以下实验技术：细胞培养；RNA 转染；RNA 提取；RT-qPCR / qPCR；ELISA；多功能酶标仪；Western blot；基因表达分析。 4、具有良好的实验设计、数据分析和实验 troubleshooting 能力。 5、重视实验可重复性、实验记录和数据质量。

具有以下一项或多项经验者优先： 1、siRNA / ASO / mRNA 等核酸药物研发经验； 2、核酸序列筛选或高通量实验经验； 3、RNA 化学修饰研究经验； 4、LNP、偶联物或其他 RNA delivery 体系经验； 5、RNAi / AGO2 / RISC 相关研究经验； 6、高通量细胞实验或自动化实验经验； 7、液体工作站、自动移液机器人或 automated assay development 经验。

我们特别看重的能力 实验科学 + 工程化思维 我们寻找的并不仅是一位能够“把实验做出来”的研究人员。 我们更希望候选人能够思考： 一个实验如何从手工 protocol 变成标准化流程？ 哪些步骤可以自动化？ 哪些变量应该被系统记录？ 如何让实验数据能够直接被 AI 模型使用？ 如何通过一次实验同时验证更多假设？ 最终我们希望候选人能参与建设一个能够让 AI 提出实验、执行实验、读取实验结果并自主迭代的下一代生物医药研发平台。

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