滴滴地图事业部(1)-算法实习生
实习兼职算法类地点:北京状态:招聘
任职要求
1.硕士或博士学历,GIS、数学、计算机、通信相关专业 2.熟悉概率论与统计,熟悉机器学习和数据挖掘算法,并有实践经验,包括但不限于LR/SVM/GBDT/Deep L…
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工作职责
利用滴滴海量的真实出行轨迹数据、机器学习和深度学习技术,对滴滴司乘轨迹行为模式、路网通行性等进行建模,相关项目可以迅速应用到实际场景(路网更新、司机异常行为检测等)中,提升滴滴用户的出行体验和平台效率。
包括英文材料
学历+
GIS+
https://www.osgeo.org/resources/learn-gis-free-complete-course/
Learning GIS, especially a modern GIS approach, can seem overwhelming, but this video explains how to take a four-step process to learn modern GIS and some tools to help you get started!
https://www.youtube.com/watch?v=n9dDsYLIx1c
Learning GIS, especially a modern GIS approach, can seem overwhelming, but this video explains how to take a four-step process to learn modern GIS and some tools to help you get started!
机器学习+
https://www.youtube.com/watch?v=0oyDqO8PjIg
Learn about machine learning and AI with this comprehensive 11-hour course from @LunarTech_ai.
https://www.youtube.com/watch?v=i_LwzRVP7bg
Learn Machine Learning in a way that is accessible to absolute beginners.
https://www.youtube.com/watch?v=NWONeJKn6kc
Learn the theory and practical application of machine learning concepts in this comprehensive course for beginners.
https://www.youtube.com/watch?v=PcbuKRNtCUc
Learn about all the most important concepts and terms related to machine learning and AI.
数据挖掘+
https://www.youtube.com/watch?v=-bSkREem8dM
Database vs Data Warehouse vs Data Lake
https://www.youtube.com/watch?v=7rs0i-9nOjo
算法+
https://roadmap.sh/datastructures-and-algorithms
Step by step guide to learn Data Structures and Algorithms in 2025
https://www.hellointerview.com/learn/code
A visual guide to the most important patterns and approaches for the coding interview.
https://www.w3schools.com/dsa/
还有更多 •••
相关职位
实习技术类
1、参与三维重建、视觉定位相关算法开发,实验测试; 2、参与视觉重定位相关项目研发,协助算法迭代、实验设计、数据分析等工作; 3、调研三维重建大模型相关前沿进展
更新于 2025-08-28北京
实习技术类
参与滴滴国际化业务搜索引擎优化相关工作,聚焦出行场景,结合大模型的深度学习技术与地理信息领域知识,通过 Query深度解析与地理信息知识融合,探索大模型在地理信息检索领域文本理解应用。包括但不限于: 1,包括通过大模型的理解和泛化能力,实现query结构化理解、纠错改写、query意图分析等任务的统一。 2,搜索结果质量评价:利用大模型完成搜索结果质量打分; 3,相关性模型:对多种类别的召回结果进行统一的相关性计算; 4,探索大模型辅助语义召回、检索排序模型的优化迭代。
更新于 2025-07-23北京