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Open nowPosted 55 days ago

材料模型算法工程师

icnl2wnnd6kf/kairosmaterialsjob10 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now材料模型算法工程师icnl2wnnd6kf/kairosmaterialsjob · Beijing, Chinese Mainland
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  4. 19.3%14 days
  5. 35.6%30 days
  6. 58.1%60 days
This job: posted 55 days ago

The posting

1.面向材料研发场景,研发与优化机器学习模型,支持材料性质预测、结构生成、配方优化及候选材料筛选。 2.探索材料结构、组成、工艺与性能的关联,开展特征构建、模型训练、评估与迭代,提升模型的预测精度和泛化能力。 3.融合文献、计算模拟及实验数据,参与材料数据集建设,解决数据稀疏、质量不一及分布差异等问题。 4.与计算化学、实验团队协作,通过主动学习、贝叶斯优化等方法推荐实验方案,推动“模型预测—实验验证—数据反馈”的研发闭环。 5.推进模型推理优化、部署及接口开发,将算法能力转化为可复用的材料研发工具。 6.跟踪 AI for Science 相关研究,复现并验证有应用价值的算法,推动其在实际材料项目中落地。

1.计算机、人工智能、数学、物理等相关专业,硕士及以上学历。 2.具备扎实的机器学习、深度学习与数理基础,熟练使用 Python,掌握 PyTorch 等主流深度学习框架。 3.熟悉图神经网络、Transformer、生成模型或几何深度学习中的至少一种,并有实际项目经验。 4.能独立完成数据处理、模型开发、训练调优、效果评估及问题分析,具备良好的代码规范与工程能力。 5.对材料与化学问题有兴趣,能够理解业务目标,与科学计算及实验团队共同定义建模任务和评价标准。 6.具备主动推进、严谨验证和跨学科协作能力,适应创业团队快速迭代的工作方式。

加分项 1.有材料性质预测、分子或晶体生成、高分子建模、机器学习势函数等相关研究或项目经验。 2.熟悉 DFT、分子动力学等计算方法,能够利用计算数据开展模型训练与验证。 3.有材料预训练模型、多模态学习、迁移学习、主动学习或不确定性估计经验。 4.有大规模模型训练、分布式计算、推理加速或模型产品化经验。 5.在相关领域发表高质量论文,或有优秀开源项目及实际落地成果。

我们提供:充足的算力资源、真实的材料研发场景、跨学科技术团队,以及无打卡、重结果的工作氛围。

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