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Open nowPosted 189 days ago

AI驱动的新一代计算化学方法与应用研究 - AI for Science

ByteDance19,261 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen nowAI驱动的新一代计算化学方法与应用研究 - AI for ScienceByteDance · Beijing, Chinese Mainland

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The clock on this job

Early applications get read.

11.4% of postings close within 7 days. Measured by our own scanner across the market. ByteDance postings stay open a median of 27 days.

Share of postings closed within
  1. 2.0%1 day
  2. 4.2%3 days
  3. 11.4%7 days
  4. 17.5%14 days
  5. 34.2%30 days
  6. 43.5%60 days
This job: posted 189 days ago

ByteDance median: 27 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！

课题介绍： 在人工智能快速发展的背景下，计算化学作为“AI for Science”的重要方向之一，近年来取得了显著进展。数据驱动方法在分子性质预测、材料筛选等任务中展现出良好潜力，但在强关联分子与复杂材料体系等关键问题上，仍面临基础性挑战：高精度方法算不大，高效率方法算不准。这一瓶颈导致计算结果与实验观测之间仍存在明显差距，限制了其在实际应用中的广泛落地。 本课题拟在此背景下，探索人工智能与计算化学方法的深度融合，发展兼具精度与效率的新一代计算化学方法，提升复杂体系的模拟能力与预测可靠性，推动计算结果向实验可验证与实际可用的方向迈进，为材料设计、催化与分子工程等领域提供关键方法支撑。

课题挑战： 1、黑盒模型在物理、化学问题中的泛化性； 2、物理先验与AI模型的有效结合； 3、适应科学场景的新模型、新优化方法的研发； 4、小样本学习在复杂化学问题中的有效性。

课题价值： 1、为复杂分子体系与材料体系提供可扩展的计算化学工具，降低高精度计算的门槛； 2、加速新材料、新催化剂及功能分子的设计与筛选过程； 3、为大规模化学空间探索提供可行路径，支持高通量计算与自动化研究。

1、2027届毕业，获得博士学位；计算机科学、机器学习、物理、化学、数学等相关专业优先； 2、有扎实数学基础及良好编程能力； 3、具有独立研究和创新的才能，能够在跨学科的环境中成长； 4、持续关注人工智能前沿发展，熟练使用各类AI工具。

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