Research Scientist Intern - Data Center AIOps & Infrastructure - Global Tech Research Program - 2027 Start (PhD)

Description du poste:

Team Introduction: The department utilizes leading algorithm capabilities to accurately connect massive users and merchants. Through recommendation, advertising, and search algorithms, we precisely match demands and facilitate transaction completion, ensuring user experience and merchant rights and interests while achieving sustainable growth of revenue businesses. Based on profound technical accumulation, we build efficient, intelligent, and reliable transaction and advertising products to comprehensively enhance revenue monetization and market share across multiple industries. We are looking for talented individuals to join us for an internship in 2027. PhD Internships at our Company aim to provide students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies. PhD internships at Our Company provides students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies. Our dynamic internship experience blends hands-on learning, enriching community-building and development events, and collaboration with industry experts. Applications will be reviewed on a rolling basis - we encourage you to apply early. Please state your availability clearly in your resume (Start date, End date). Topic Content: As intelligent computing and the AIDC industry develop rapidly, data center rack power density continues to rise. Traditional infrastructure struggles with heat exchange efficiency, water-saving, and energy use. At the same time, large amounts of maintenance data are underused, and maintenance relies heavily on human experience, making it hard to meet the industry's quality and policy demands. This topic focuses on two main areas: technology innovation and smart operation. First, it aims to innovate in liquid cooling, water-saving or water-free cooling, and power supply and energy storage to overcome current limits, meet high-density power needs, and comply with carbon reduction and water-saving policies. Second, it builds an AI agent system for data center maintenance that uses large AI models to learn from vast, varied multimodal maintenance data. This system enables multiple AI agents to work together to automate everything from monitoring and diagnosis to repair, creating an intelligent loop to optimize the power usage effectiveness (PUE) and shifting maintenance from "reacting after problems" to "predicting and fixing them automatically". By combining new hardware technology with AI-driven maintenance, the project seeks to improve data center energy efficiency, reliability, and operation efficiency. Topic Challenges: 1. Multi-technology collaborative innovation: Liquid cooling technology must overcome challenges in efficient heat transfer and system reliability. Water-saving cooling sources need to achieve low water-efficiency temperature (WET) and coordinate dry and wet cooling methods. Power supply, distribution, and energy storage must solve source-grid-load-storage matching issues and enhance full-process efficiency. Coordinating innovations across these areas is highly complex. 2. Multi-agent collaboration system design: A multi-agent cooperation framework is required for managing complex operation and maintenance processes, enabling end-to-end autonomous execution from monitoring and diagnosis to automatic repair. Integrating these technologies is highly challenging. 3. Intelligent PUE optimization closed loop: Intelligent control of HVAC and power systems should be based on time sequence prediction and reinforcement learning to surpass human expert-level optimization. This demands advanced algorithms and strong engineering implementation. 4. Root cause analysis of heterogeneous data: Large models must automatically identify fault root causes and build knowledge from massive heterogeneous monitoring data, requiring high model understanding and generalization capability. 5. Technology implementation and adaptation: Hardware innovation must comply with policies and fit industry needs, while AI operations must integrate with existing platforms and tools. Successfully combining and implementing these poses significant challenges. Topic Value: 1. Solve key technical problems in data centers and increase global competitiveness. 2. Enable water-saving and low-carbon operation, ensuring data centers run efficiently, stably, and in compliance through AI-driven maintenance

Profil requis du candidat:

Minimum Qualifications 1. Currently pursuing PhD in Mechanical/Electrical Engineering, or a related technical discipline; Preferred Qualifications 1. Relevant experience of advanced electrical/liquid-cooling technologies design and deployment