Computational AI Scientist (MLFF Development) AI科学家（机器学习力场）

Develop, adapt, and optimize graph neural network (GNN)–based universal machine learning force field code. Collaborate with team members to ensure code modularity, efficiency, and reproducibility. Apply methods flexibly to new scientific problems, with a focus on materials science (preferred), but openness to candidates from other domains. Contribute to maintaining clear documentation and scalable workflows.

PhD or equivalent experience in Computer Science, AI/ML, Materials Science, Physics, Chemistry, or related technical field. Strong programming skills in Python and experience with machine learning frameworks. Demonstrated ability to work with GNNs or similar architectures in scientific or applied contexts. Scientific background preferred (materials> bio > no prior science experience). Excellent communication skills in professional English. Strong analytical and problem-solving abilities, with capacity to meet deadlines in a fast-paced environment. 职责 开发、改写并优化基于图神经网络（GNN）的通用机器学习力场代码。 与团队成员协作，确保代码的模块化、效率与可重复性。 灵活地将方法应用于新的科学问题，重点面向材料科学（优先），同时对其他领域候选人保持开放。 参与维护清晰的文档与可扩展的工作流程。 任职资格 计算机科学、人工智能/机器学习、材料科学、物理、化学或相关技术领域博士学位或同等经验。 扎实的 Python 编程能力，并具备使用机器学习框架的经验。 在科学或应用场景中具备使用 GNN 或类似架构的能力。 具备科学背景者优先（材料 > 生物 > 无相关经验）。 优秀的专业英语沟通能力。 具备强大的分析与问题解决能力，能够在快节奏环境中按时完成任务