Expoint - all jobs in one place

מציאת משרת הייטק בחברות הטובות ביותר מעולם לא הייתה קלה יותר

Limitless High-tech career opportunities - Expoint

Amazon 应用科学家实习生:多模态检索与生成方向 Shanghai AI Lab 
Thailand, Bangkok, Bangkok 
672334838

16.09.2024
DESCRIPTION

Internship Direction: Multi-Modal Retrieval and Generation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.

BASIC QUALIFICATIONS

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. 有良好的机器学习或分析性工作的相关背景。
3. 熟练使用Python,熟悉近期的流行深度学习框架。
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 background in machine learning or relevant analytical studies.
3. Familiar with deep learning domains as computer vision or natural language processing, interested in challenges from real-world applications, e.g. robustness and transferability. Being the first author on papers in top conferences and journals is preferred.
4. Excellent algorithms and programming skills, proficient in Python and recent deep learning frameworks.
5. Can guarantee at least 4 months internship and 4 days a week.


PREFERRED QUALIFICATIONS

1. 可以进行 6 个月及以上的实习,每周至少工作 4 天。
2. 有机器学习/深度学习论文发表经历者优先。
3. 有多模态研究经验者优先。1. Have past publication experience in top ML/DL conferences or journals.
2. Have experience in multi-modal research.