Research Scientist Graduate (Data Center AIOps & Infrastructure) - 2027 Start (PhD)
ByteDance, Singapore
What you will do
- 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.
- 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.
- 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.