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Open nowPosted 73 days ago

通用基础模型研究员 - AI for Science

ByteDance19,269 open roles

Where
Shenzhen, Guangdong, Chinese Mainland
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Your applicationOpen now通用基础模型研究员 - AI for ScienceByteDance · Shenzhen, Guangdong, Chinese Mainland

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The clock on this job

Early applications get read.

11.5% of postings close within 7 days. Measured by our own scanner across the market. ByteDance postings stay open a median of 9 days.

Share of postings closed within
  1. 2.0%1 day
  2. 4.3%3 days
  3. 11.5%7 days
  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 73 days ago

ByteDance median: 9 days open

The posting

团队介绍：AI for Science 团队专注于科学计算与物质发现领域的前瞻技术探索，围绕生物和材料领域基础模型、量子化学、AI分子动力学、高通量实验技术等方向，用 AI 推动科学领域的研究范式突破，也在实际应用场景中创造价值。我们希望用 AI 赋能科学，探索 AI 在科研领域的变革，拓展人类认知边界！

1、面向AI for Science领域，特别是AI for Materials领域，研发可跨科学任务、跨材料体系、跨数据模态和跨学科迁移的通用基础模型，探索科学数据统一表征、大规模预训练、材料性质预测与材料候选生成等技术； 2、研究科学多模态学习、大语言模型、图与几何深度学习、生成模型、强化学习等前沿方向，提升模型的泛化能力、数据效率及可扩展性； 3、面向不同材料体系、化学反应等科学对象，融合文献、模拟及实验中的多模态数据，构建统一学习与跨模态对齐方法； 4、与机器学习、计算化学和材料高通量实验室等跨学科团队协作，开展原创性研究，产出具有行业影响力的算法、系统、专利及高水平学术成果。

1、2027届获得博士学位，计算机科学、人工智能、统计学、应用数学、计算化学、计算物理等相关专业优先； 2、具备扎实的机器学习基础，深入理解深度学习及基础模型相关技术，在以下至少一个方向具备系统研究和成熟成果：大语言模型预训练与后训练、Agent、多模态学习、图与几何深度学习； 3、具备优秀的研究能力和代表性成果，在NeurIPS、ICML、ICLR等机器学习会议，或Nature、Science及其子刊，以及相关领域权威期刊发表过高水平论文； 4、熟悉大规模模型训练、数据工程或模型评测中的一项或多项，有科学基础模型、大语言模型、多模态模型或生成式模型的大规模训练经验，能够推动研究原型向稳定系统演进； 5、具备较强的问题抽象与技术创新能力，能够从复杂科学问题中提炼可计算、可验证的机器学习问题；对AI for Science和通用智能驱动科学发现具有长期热情，具备攻克开放性难题的韧性，以及良好的跨学科沟通与团队协作能力； 6、熟悉分子、材料、化学反应等一种或多种科学数据；在真实工业与科研场景中取得过有影响力的成果；具备模型与实验、计算模拟或科学数据库协同闭环的实践经验。

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