(Senior) Researcher - Data Center Network

Company overview:

Huawei is a leading global information and communications technology (ICT) solutions provider. Through our constant dedication to customer-centric innovation and strong partnerships, we have established leading end-to-end capabilities and strengths across the carrier networks, enterprise, consumer, and cloud computing fields. Our products and solutions have been deployed in over 170 countries serving more than one third of the world’s population.

With 29 sites across Europe and 2000 researchers, Huawei’s European Research Institute (ERI) oversees fundamental and applied technology research, academic research cooperation projects, and strategic technical planning across our network of European R&D facilities. Huawei’s ERI includes the Zurich Research Center (ZRC), located in Zurich, Switzerland.

The research work of the lab will be carried out not only by Huawei’s internal research staff but also by our academic research partners in universities across Europe. The lab will provide an open research environment where academics will be encouraged to visit and work on fundamental long-term research alongside Huawei staff in an environment that, like the best universities and research institutes, is open and conducive to such world-leading scientific work.

In order to strengthen our team of Data Center Network (DCN) we are looking for a:

(Senior) Researcher in Data Center Network

In this role, you will be responsible for the research and explore the development direction of DCN services and architecture, key DCN algorithms and protocols, help the team to complete key technological innovations, and build key technical breakpoints.

Responsibilities

Keep track of or delve into the leading-edge DCN technologies in the academia and industry
Conduct research on the top-level architecture and key technologies of the next-generation DCNs
Collaborate with other members in the lab to complete the design and implementation of new DCN techniques
The scope includes but is not limited to network topology, traffic control technology, software-defined networks, optical and electrical hybrid networks, in-network computing, etc.
Optimize the performance and efficiency of parallel computing systems, especially the large-scale AI clusters, mainly from the perspective of the integration of network, computing, and storage systems.

Requirements

Doctoral Degree in Computer Science, Electronic Engineering, Artificial Intelligence, Automation, Mathematics, Physics and other related majors.
Candidate with working experience in both industry and academia is preferred.
Be familiar with data center networks and high-performance interconnection technology systems.
Be familiar with performance or efficiency optimization of parallel computing systems.
Have background in large-scale distributed deep learning and be familiar with corresponding infrastructure and key technologies.
Self-motivated and highly ambitious, good team spirit and cross-cultural communication skills.

More Specifically

Have research experience in the field of modern network technology or high-performance interconnection, be familiar with cloud DCN technologies, including network architecture, resource pooling techniques, optical network techniques, etc.
Be familiar with performance or efficiency optimization of parallel computing systems. Possess the capability to do research on distributed machine/deep learning, distributed storage, high-performance computing systems, including network architecture, networking solutions, and the design of new device forms.
Be familiar with the infrastructure and key technologies of large-scale training/inference, and have systematic thinking on how to improve the system scale and efficiency (Optional).
Be familiar with network theory and optimization algorithms, focusing on DCN business service assurance technology, network congestion control, traffic scheduling/forecasting, TCP/RDMA acceleration, and low-latency assurance for DCN, etc. Possess the capability to do research on next-generation network architecture and core technologies for network modeling and optimized control.
Have experience on conducting innovative research in the fields of new data center architectures, new topologies, new protocols, new hardware, and algorithms, such as CXL bus protocol extensions, non-CLOS topologies, GPU cluster interconnections, and DPU offloading, among other new technological directions