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Open nowPosted 123 days ago

博士后-生物结构生成AI大模型方向

c0fu1j3a9m0/referral33 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now博士后-生物结构生成AI大模型方向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 123 days ago

c0fu1j3a9m0/referral median: 1 days open

The posting

  • AI模型应用与开发： 负责将前沿AI4Biology模型（如蛋白语言模型 ESM3、结构预测模型 AlphaFold3/Protenix、蛋白设计模型 RFdiffusion 等）应用于实际科研问题，包括蛋白质从头设计、抗体设计与优化、酶工程改造等方向。
  • 功能机制研究： 结合AI预测与计算生物学手段（如结构建模、MD模拟、自由能计算），开展蛋白质功能机理研究，解析序列-结构-功能关系，阐明关键相互作用机制。
  • 干湿实验闭环： 与实验团队紧密协作，推动"AI设计—计算筛选—实验验证—数据反馈迭代"的完整闭环，对模型预测结果进行系统性评估与优化，确保研究成果可落地。
  • 前沿追踪与创新： 跟踪AI4Science领域最新进展（如生成式模型、扩散模型、多模态生物大模型），独立提出研究课题，产出高水平学术论文与可复用的方法/工具。

- 博士学历（或即将获得博士学位），计算生物学、生物信息学、结构生物学、计算化学、人工智能等相关专业。 - AI模型经验： 熟悉主流AI4Biology模型及其应用场景，包括但不限于： 蛋白语言模型：ESM3、ESMFold、ProteinMPNN 等； 结构预测模型：AlphaFold3（Protenix）、Chai-1、Boltz 等； 蛋白/抗体设计模型：RFdiffusion、RFantibody、DiffAb 等； 具备上述至少一类模型的实际使用或开发经验，理解其原理与局限性。 - 编程与工程能力： 熟练掌握 Python 及深度学习框架（PyTorch），熟悉 Hugging Face 生态，具备模型微调、推理部署及大规模计算任务管理经验，熟悉 Linux/超算环境。 - 专业基础： 具备扎实的结构生物学或生物物理学基础，熟悉蛋白质结构分析与可视化工具（PyMOL、ChimeraX），熟悉常用生物学数据库（PDB、UniProt、AlphaFold DB）。 - 科研素养： 以第一作者发表过高水平学术论文，具备独立开展科研工作的能力、良好的英文读写能力及跨团队协作沟通能力。 - 加分项： 有 AI 设计与湿实验验证闭环的完整项目经验（如设计的蛋白/抗体经实验验证成功）； 有 MD 模拟、自由能计算与 AI 模型结合的经验（如 AI 预测结构后进行 MD 精修或构象采样）； 有扩散模型、生成式模型或几何深度学习相关方法开发经验； 熟悉抗体-抗原相互作用、酶催化机制等具体生物学问题背景。

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