微软学术合作 2022-04-29 17:00
微软亚洲研究院与微软总部联合推出的“星跃计划”科研合作项目邀请你来报名！本次“星跃计划”报名再次新增了来自微软 E+D (Experiences + Devices) Applied Research 全球总部的新项目，欢迎大家关注与申请！还在等什么？加入“星跃计划”，和我们一起跨越重洋，探索科研的更多可能！
该计划旨在为优秀人才创造与微软全球总部的研究团队一起聚焦真实前沿问题的机会。你将在国际化的科研环境中、在多元包容的科研氛围中、在顶尖研究员的指导下，做有影响力的研究！
目前还在招募的跨研究院联合科研项目覆盖智能推荐、用户行为检测等领域。研究项目如下：Responsible Recommender System, DNN-based Detection of Abnormal User Behaviors。星跃计划开放项目将持续更新，请及时关注获取最新动态！
星跃亮点
同时在微软亚洲研究院、微软全球总部顶级研究员的指导下进行科研工作，与不同研究背景的科研人员深度交流
聚焦来自于工业界的真实前沿问题，致力于做出对学术及产业界有影响力的成果
通过线下与线上的交流合作，在微软了解国际化、开放的科研氛围，及多元与包容的文化
申请资格
硕士、博士在读学生（具体参考项目要求）；延期（deferred）或间隔年（gap year）学生
可全职在国内工作 6-12 个月
各项目详细要求详见下方项目介绍
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还在等什么？
快来寻找适合你的项目吧！
Responsible Recommender System
Pretrained language models such as BERT and UniLM have achieved huge success in many natural language processing scenarios. In many recommendation scenarios such as news recommendation, video recommendation, and ads CTR/CVR prediction, user models are very important to infer user interest and intent from user behaviors. Previously, user models are trained in a supervised task-specific way, which cannot achieve a global and universal understanding of users and may limit they capacities in serving personalized applications.
In this project, inspired by the success of pretrained language models, we plan to pretrain universal user models from large-scale unlabeled user behaviors using self-supervision tasks. The pretrained user models aim to better understand the characteristics, interest and intent of users, and can empower different downstream recommendation tasks by finetuning on their labeled data. Our recent work can be found at https://scholar.google.co.jp/citations?hl=zh-CN&user=0SZVO0sAAAAJ&view_op=list_works&sortby=pubdate.
Research Areas
Recommender Systems and Natural Language Processing
Qualifications
Ph.D. students majoring in computer science, electronic engineering, or related areas
Self-motivated and passionate in research
Solid coding skills
Experienced in Recommender Systems and Natural Language Processing
DNN-based Detection of
Abnormal User Behaviors
Are you excited to apply deep neural networks to solve practical problems? Would you like to help secure enterprise computer systems and users across the globe? Cyber-attacks on enterprises are proliferating and oftentimes causing damage to essential business operations. Adversaries may steal credentials of valid users and use their accounts to conduct malicious activities, which abruptly deviate from valid user behavior. We aim to prevent such attacks by detecting abrupt user behavior changes.
In this project, you will leverage deep neural networks to model behaviors of a large number of users, detect abrupt behavior changes of individual users, and determine if changed behaviors are malicious or not. You will be part of a joint initiative between Microsoft Research and the Microsoft Defender for Endpoint (MDE). During your internship, you will get to collaborate with some of the world’s best researchers in security and machine learning.
You would be expected to:
Closely work with researchers in China and Israel towards the research goals of the project.
Develop and implement research ideas and conduct experiments to validate them.
Report and present findings.
Microsoft is an equal opportunity employer.
Research Areas
Software Analytics, MSR Asia
https://www.microsoft.com/en-us/research/group/software-analytics/
Microsoft Defender for Endpoint (MDE)
This is a Microsoft engineering and research group that develops the Microsoft Defender for Endpoint, an enterprise endpoint security platform designed to help enterprise networks prevent, detect, investigate, and respond to advanced threats
https://www.microsoft.com/en-us/security/business/threat-protection/endpoint-defender
Qualifications
Must have at least 1 year of experience applying machine learning/deep learning to real world/ research problems
Demonstrated hands on the experience with Python through previous projects
Familiarity with Deep Learning frameworks like PyTorch, Tensorflow, etc
Keen ability for attention to detail and a strong analytical mindset
Excellent in English reading and reasonably good in English communications
Advisor’s permission
Those with the following conditions are preferred:
Prior experience in behavior modeling
Prior experience in anomaly detection
Security knowledge a plus
申请方式
符合条件的申请者请填写下方申请表：
https://jinshuju.net/f/LadoJK
