Amazon 应用科学家实习生：网络与关系型数据机器学习方向 Shanghai AI Lab 

Thailand, Bangkok, Bangkok 

153204756

Internship Direction: Relational Machine Learning亚马逊上海人工智能研究院是深度学习研究的领军团队之一。我们的研究包括但不限于以下方向：深度学习基础理论、自然语言处理、计算机视觉、图机器学习、高性能计算、智能推荐系统、反欺诈与风控、知识图谱构建以及智能决策系统等。研究院积极推进深度学习的开源生态建设，其主攻的深度图网络DGL（Deep Graph Library）开源库是该领域的领跑平台。我们致力于通过跨领域的研究和合作，推进人工智能技术的边界，并通过开源项目的贡献，促进全球研究社区的共同进步。除了每天能与亚马逊上海人工智能研究院的同事们交流外，实习生还将有机会和亚马逊其他部门的同事、上海一流高校的顶级教授、和来自世界各地的一流专家合作，如Alon Halevy, Christos Faloutsos、Stefano Soatto、Pietro Perona、George Karypis、Thomas Brox、 David Wipf、付彦伟、张伟楠、张牧涵、邱锡鹏、张岳、张峥等。The advancements in deep learning technology are driving rapid development in the field of artificial intelligence, closely integrating multiple disciplines such as computer vision, natural language processing, graph and network processing, systems engineering, and optimization theory. Moreover, numerous deep learning open-source projects have not only accelerated the pace of academic research but also promoted the commercialization and social application of technology.

1. 计算机、数学、统计学以及相关专业在校研究生或博士生。
2. 对深度学习的数学知识了解充分。
3. 熟练使用Python，熟悉至少一种常见深度学习框架（PyTorch，Tensorflow，JAX等）。
4. 可以保证至少 4 个月的实习，每周至少工作 4天。1. Master or PhD students in Computer Science, Mathematics or Statistics.
2. Good understanding of math skills required in Deep Learning.
3. Proficient in Python and familiar with at least one deep learning frameworks (PyTorch, Tensorflow, JAX etc.).
4. Can guarantee at least 4 months internship and 4 days a week.

1. 有机器学习论文发表经历者优先。
2. 对关系型数据建模（比如Gradient Boosting Tree，图神经网络，网络嵌入等）有经验者优先。1. Have past publication experience in top ML conferences or journals.
2. Good understanding on popular ML techniques for relational data such as gradient boosting tree, graph neural networks, network embedding, etc.