来自义大利的老板今天请我帮忙宣传招生, 竟然直接点名PTT studyabroad板... 个人在 KAUST 两年来对他的印象一直很好, 确实有实力也能给学生帮助 所以满愿意协助宣传, 若有相关问题欢迎推文或站内信询问 关於 KAUST 的文章板上也能找到几篇, 请 /KAUST 搜寻即可 以下代PO ----------------------------------------------------------------------- We are looking for two PhD students with a strong interest to work at the intersection of distributed systems and machine learning. The successful candidates will be expected to devise scalable and robust distributed algorithms and systems for (deep) machine learning models. Machine learning (ML) is the foundation of many of today's applications, from classification systems for image and speech recognition, to guiding self-driving cars. Due to the large data volume and ubiquitous data sensing from edge devices, e.g., smart phones, ML is shifting from the centralized cluster setting to distributed systems: in a setting known as federated learning, data holders collaborate to train a global model by sharing their parameter updates. This setting is challenging because of many real-world factors like resource heterogeneity, varying network connectivity, and different levels of client participation, which make it hard to learn high quality, robust models. Besides, these models can take days or even weeks to train! One research direction is to design algorithms to optimize the trade-off between computation and communication of distributed learners and for compressing communication of model updates (see our GRACE project: https://sands.kaust.edu.sa/project/grace ). A second direction considers opportunities for accelerating deep learning computations via new hardware architectures (whether on-device, cloud offload or edge/in-network computing), including FPGAs in collaboration with Dr. Suhaib Fahmy at the University of Warwick, UK. Another is focused on designing fairness-aware algorithms to mitigate system-level causes that bias the model towards certain clients over others. Having a strong background in machine learning is essential. Being familiar with distributed learning systems or federated learning is helpful but not required. Interested? Please get in touch with [email protected] to discuss your application. But make sure you read this first: https://mcanini.github.io/students.html If you have a BS degree, apply to our combined MS/PhD program. If all goes well, you will get an MS degree in 1.5 years (3 semesters), and will then continue towards your PhD. If you have an MS degree already, apply directly to the PhD program. SANDS Lab At SANDS Lab (http://sands.kaust.edu.sa/ ), we perform world-class research in the design, implementation, deployment, and analysis of large-scale networked systems. We are a group of systems builders and we currently focus on the scalability, efficiency and robustness of deep learning. Much of our work is experimental: to validate our proposed concepts, we build system prototypes that directly improve the lives of real users. Located on the shores of the Red Sea, KAUST is a fantastic place to study and live at. Every student is fully funded with a generous fellowship (~$30,000 USD/year) that covers a stipend, housing and more. Information on application requirements and on the university is available at this page: https://www.kaust.edu.sa/en/study --

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