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Open nowPosted 43 days ago

生成模型科研实习生

sii-czxy/campus170 open roles

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Your applicationOpen now生成模型科研实习生sii-czxy/campus

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Share of postings closed within
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  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 58.1%60 days
This job: posted 43 days ago

The posting

1. 阅读并整理 AI for Biomedical Science、蛋白质生成模型、抗体与 TCR 设计、多模态生成模型等方向的前沿论文和技术资料，跟踪领域最新研究进展。 2. 复现和调试开源生物医药生成模型，包括扩散模型（Diffusion Model）、Flow Matching、蛋白质语言模型等，在标准数据集上完成模型运行、基准测试和性能对比分析。 3. 参与生物医药相关生成模型的设计与训练工作，探索基于人工智能的抗体、TCR、蛋白质及治疗分子生成方法。 4. 协助构建和完善模型评测体系，参与 Benchmark 任务设计、评价指标制定、实验方案优化及结果分析。 5. 根据项目进展参与真实生物医药研发流程中的数据分析与实验验证，包括模型预测结果评估、候选分子筛选及湿实验验证支持。 6. 参加课题组讨论和阶段性研究汇报，整理实验记录、技术文档和研究成果。

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、深度学习和生成模型基本方法，能够完成模型运行、实验调试和数据分析。 4. 对以下方向具有研究兴趣：AI for Drug Discovery；蛋白质语言模型；抗体/TCR 生成模型；扩散模型与 Flow Matching；多模态生物医药人工智能。 5. 能够阅读英文技术论文和科研资料，具备基础实验设计、代码复现和结果分析能力。 6. 具备以下经历者优先：深度学习、生成模型或大模型相关项目经验；蛋白质结构预测、生物信息学分析经验；PyTorch、Transformers 等深度学习框架使用经验；论文复现、算法竞赛或开源项目经历。 7. 具备较强的问题分析能力和自主学习能力，能够参与模型训练、实验优化和科研探索工作。 8. 对“如何利用生成式人工智能设计新型生物分子，加速抗体药物、TCR 细胞治疗和创新药物研发”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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