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Open nowPosted 43 days ago

时序建模与预测算法研究实习生

sii-czxy/campus170 open roles

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Your applicationOpen now时序建模与预测算法研究实习生sii-czxy/campus

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  6. 57.8%60 days
This job: posted 43 days ago

The posting

1. 构建时序研究数据集：整理项目中的文本信号、事件记录、实体关系及相关数值数据，完成时间对齐、缺失处理、特征构建和质量检查，明确不同信息的实际可获得时间。 2. 复现预测模型与基线方法：阅读时序分析、事件预测等方向论文，复现统计学习、机器学习或深度学习方法，建立可重复运行的预测实验流程。 3. 探索多源信息融合方法：研究文本信息、事件信号与数值时序数据的融合方式，参与时序表示学习、多时间尺度建模及预测不确定性估计等算法研究。 4. 开展严格实验评估：按照时间顺序组织训练与测试，进行滚动验证、消融实验和跨时期稳健性分析，关注未来信息泄漏、过拟合及数据分布变化等问题。 5. 验证预测模型应用价值：将有效方法接入事件感知与预测系统，评估模型对事件追踪和判断更新的支持能力；在投资相关场景中，参与信号有效性分析与策略回测，考虑信息延迟、交易成本等实际约束。

1. 招募对象：本科生、硕士研究生。 2. 计算机、人工智能、数学、统计、运筹学、计量经济学等相关专业背景优先。 3. 具备概率统计、线性代数和机器学习基础，了解时间序列分析、优化方法或计量经济学者优先。 4. 具备 Python 数据处理与建模能力，能够完成数据清洗、模型训练、实验分析和结果可视化；熟悉至少一种机器学习或深度学习框架。 5. 有时序预测、事件建模、异常检测、算法竞赛、论文复现或量化研究经历者优先，不要求具备金融从业或交易经验。 6. 能够阅读英文论文和技术资料，重视实验设计、数据质量和误差分析，而不仅关注单次实验结果。 7. 具备良好的实验记录和科研习惯，能够稳定投入项目研发并参与阶段性汇报。 8. 不要求已有正式科研经历或论文发表，更关注数学基础、实验能力和研究兴趣。 9. 实习安排：线下科研实习；周期6个月以上；每周投入30小时以上。

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