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Open nowPosted 40 days ago

AI+核聚变科研实习生

sii-czxy/campus170 open roles

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  4. 18.1%14 days
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  6. 44.6%60 days
This job: posted 40 days ago

The posting

1. 阅读并整理 AI for Science、可控核聚变、世界模型、强化学习、生成模型等方向的前沿论文和技术资料，根据兴趣选择具体研究方向开展探索。 2. 围绕以下方向之一参与研究与实验： （1）聚变世界模型方向：结合托卡马克实验数据、物理方程和仿真数据，参与构建面向聚变装置的世界模型，探索利用多模态模型、物理信息神经网络（PINN）、神经算子等方法建模等离子体演化过程。 （2）等离子体智能控制方向：基于仿真环境或聚变世界模型，与智能体进行交互，探索利用强化学习、模型预测控制（MPC）等方法学习高效、安全的等离子体控制策略。 （3）聚变装置智能设计方向：利用生成模型、智能体、优化算法等人工智能方法，参与托卡马克位形、磁线圈等关键部件的自动化设计与优化。 3. 参与相关数据处理、模型训练、实验设计和结果分析工作，协助搭建实验流程并完成性能评估。 4. 参加课题组讨论和阶段性汇报，整理实验结果和技术文档，根据研究进展参与论文或项目成果整理。

1. 招募对象：本科生、硕士研究生。 2. 计算机、人工智能、自动化、控制科学、数学、物理、能源、电子信息等相关专业背景优先。 3. 具备 Python 编程基础，了解机器学习、深度学习或强化学习基本方法，能够使用常见 AI 框架完成基础实验。 4. 对以下方向具有研究兴趣：AI for Science（人工智能驱动科学研究）；物理智能与世界模型；强化学习与智能控制；生成模型与智能优化；AI Agent 与科学设计自动化。 5. 具备以下相关经验者优先：世界模型、多模态大模型预训练与微调；物理信息神经网络（PINN）、神经算子等物理 AI 方法；强化学习、模型预测控制（MPC）等控制方法；生成模型、智能体、优化算法及设计自动化相关研究。 6. 能够阅读英文论文和技术资料，具备实验设计、代码实现和结果分析能力。 7. 有物理、等离子体、核聚变等相关知识背景，或具备新能源、机器人、科学计算等交叉研究经历者优先。 8. 具备较强的科研探索能力和团队协作意识，能够主动分析问题、推进实验，并稳定投入科研时间。 9. 对“如何利用人工智能加速可控核聚变研究，实现复杂科学系统的预测、控制与自主设计”这一方向具有持续兴趣。 10. 实习安排：线上线下结合；周期3–6个月；每周投入20–30小时。

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