Join Computing Infrastructure

The training of new AI models, as well as their deployment for inference, is transforming the design of computer networks. In this project, you will investigate innovative networking solutions for frontier AI systems, with a particular focus on the architecture of network switches. You will explore the role of the network switch in AI systems with sub-1 ns latency between accelerators, including how operations are partitioned between switches and edge devices. Building on this analysis, you will develop novel networking solutions that accelerate frontier AI systems.

Prospective candidates will be judged according to how well they meet the following criteria:

• A first class or strong upper second-class undergraduate degree with honours (or equivalent) in Engineering or Computer Science
• Excellent English written and spoken communication skills
• Excellent knowledge in computer networks
• Excellent hardware design skills (Verilog)
• Excellent programming skills (C/C++ and/or python)

The following skills are desirable but not essential:

• A distinction or first class honours Masters degree in Engineering or Computer Science
• Previous research experience in the subject area (e.g., final year project, internship)
• Previous experience in FPGA or ASIC design
• Previous experience in network simulation
• Previous experience writing for publication

More Details here

Project: Merging Datacentres into Manufacturing and Process Automation

Modern manufacturing and process automation floors are evolving into complex data ecosystems, mirroring the operational challenges of datacentres. As sensors generate vast data streams for AI-driven cell operations (Industrial-grade AI), the factory becomes a distributed computing environment requiring low-latency response, resilience, and energy efficiency. This research project addresses the challenge of developing e-Infrastructure for manufacturing that combines datacentre performance with the constraints of an AI-driven factory.

You will research emerging computing solutions for AI-driven manufacturing, covering aspects of network design and resilience, workload scheduling and deployment, and energy efficiency. 

Prospective candidates will be judged according to how well they meet the following criteria:

• A first class or strong upper second-class undergraduate degree with honours (or equivalent) in Engineering or Computer Science
• Excellent knowledge in computer networks
• Excellent programming skills (C/C++, Python and scripting languages)
• Excellent English written and spoken communication skills

The following skills are desirable but not essential:

• A distinction or first class honours Masters degree in Engineering or Computer Science
• Previous research experience in the subject area (e.g., final year project, internship)
• Good knowledge in distributed systems
• Previous experience in network simulation
• Previous experience in network drivers and tools
• Previous experience in distributed AI workloads
• Previous experience in industrial systems
• Previous experience writing for publication

More details here - Note the special application link

The evolving demands of distributed AI workloads differ significantly from traditional network traffic patterns. Emerging AI traffic patterns require bulk data movement between GPU clusters, demanding low latency, high bandwidth, and zero tolerance for timing jitter and data loss. The project will explore the evolution of intra- and inter-data centre interconnects, supporting both traditional and AI traffic. It will research workload distribution across centralized cloud, network edge, and Points of Presence (PoPs) and develop innovative strategies to evolve Wide Area Networks (WAN) over the next decade.

You will research emerging networking solutions for AI-workloads, covering aspects such as network design and resilience, interconnect-hardware co-design, and cost-effective network evolution. 

Prospective candidates will be judged according to how well they meet the following criteria:

• A first class or strong upper second-class undergraduate degree with honours (or equivalent) in Engineering or Computer Science
• Excellent knowledge of computer networks
• Excellent programming skills (C/C++, Python and scripting languages)
• Excellent English written and spoken communication skills

The following skills are desirable but not essential:

• A distinction or first class honours Masters degree in Engineering or Computer Science
• Previous research experience in the subject area (e.g., final year project, internship)
• Previous experience in network simulation
• Previous experience in network drivers and tools
• Previous experience in distributed AI workloads
• Previous experience writing for publication

The Oxford Martin AI Governance Initiative (AIGI) and the Department of Engineering Science are offering fully funded studentships, covering tuition and a stipend, to support DPhil researchers at the University of Oxford working in the field of technical AI governance.

Technical AI Governance (TAIG) DPhil Studentships are a first-of-their-kind opportunity for doctoral study that are designed to train researchers who can bridge the gap between advanced AI systems and effective governance. Studentships are available on a fully-funded, full-time basis, but remote and part-time arrangements may be considered for exceptional candidates, ensuring flexibility without compromising research quality.

As part of this studentship, you will explore aspects of compute infrastructure governance or AI system resilience, covering elements such as computer architecture, cloud computing, computer networks, accelerators architecture and AI. 

Prospective candidates will be judged according to how well they meet the following criteria:

• A first class or strong upper second-class undergraduate degree with honours (or equivalent) in Engineering or Computer Science
• Excellent English written and spoken communication skills
• Excellent knowledge in computer networks or computer architecture
• Excellent knowledge in machine learning and AI
• Excellent programming skills (C/C++ and/or python)

The following skills are desirable but not essential:

• A distinction or first class honours Masters degree in Engineering or Computer Science
• Previous research experience in the subject area (e.g., final year project, internship)
• Previous experience writing for publication
• Previous experience in policymaking 
• Previous experience in hardware design OR
• Previous experience in network or computer architecture simulation OR
• Previous experience in cloud computing OR
• Previous experience in accelerators design

More Details here