Research Assistant or Associate in AI Model Optimisation for Edge Devices & NVIDIA Holoscan Sen

Research Assistant or Associate in AI Model Optimisation for Edge Devices & NVIDIA Holoscan Sensor Bridge Integration

Job number ENG
03990 Faculties Faculty of Engineering Departments Department of Electrical and Electronic Engineering Salary or Salary range £45,399- £59,484 per annum Location/campus South Kensington Campus
- Hybrid Contract type work pattern Full time
- Fixed term Posting End Date 13 Aug 2026

We are seeking a Research Assistant or Research Associate to work at the intersection of AI model optimisation, GPU kernel development and FPGA-based hardware integration. The project targets the seamless integration of computer vision FPGA-based IPs with NVIDIA's Holoscan Sensor Bridge - a cutting-edge technology enabling low-latency, high-throughput streaming between sensors and edge GPU platforms.

The project involves Imperial College London and an industrial partner, Heronic Technologies (https://(Use the "Apply for this Job" box below). ), aiming to revolutionise the "Sense-Decide" pipeline in edge automation.

You will contribute to building a system that tightly couples custom FPGA-based AI-ISP accelerators with NVIDIA's GPU-powered edge platforms, with a focus on minimising latency while maintaining high performance and scalability. A significant part of the work will involve AI model optimisation and the customisation of edge GPU kernels to push system performance to its limits.

This is a genuinely multidisciplinary challenge, spanning AI model design, low-level GPU kernel engineering, and hardware-software co-design - an opportunity to advance the state of the art in how AI signal processing systems are built and deployed.

• Investigating and developing system architectures that demonstrate low-latency, easy integration of custom AI-ISP accelerators with GPU platforms via NVIDIA's Holoscan Sensor Bridge
• Developing and evaluating the full system under object detection applications, assessing performance across latency and detection accuracy metrics
• Implementing models in machine learning frameworks (e.g. PyTorch) and applying hardware-aware efficiency metrics to evaluate energy, memory, and latency trade-offs
• Contributing to research publications and presenting results at academic conferences
• Collaborating closely with Prof Christos Bouganis and the team at Heronic Technologies, who are developing the FPGA-based AI-ISP accelerator
• Helping to bridge the gap between academic research and industrial impact in energy-efficient AI

• A strong background in GPU programming, machine learning, digital hardware design, computer engineering, applied mathematics, or a closely related field
• Experience with software engineering for scientific computing or machine learning (e.g. PyTorch), GPU programming and/or digital hardware design (e.g. Verilog)
• Ability to analyse complex systems, develop new models, and communicate research clearly
• A collaborative mindset and genuine enthusiasm for advancing energy-efficient AI

An interest in one or more of the following areas is desirable:

• Efficient machine learning and AI model optimisation
• GPU kernel programming and optimisation
• Digital hardware or FPGA architectures

Qualifications

• Research Associate: A PhD in machine learning, computer engineering, applied mathematics, or a closely related discipline -- or equivalent research or industry experience
• Research Assistant: A master's degree (or equivalent) in a relevant discipline -- or equivalent experience. Candidates who have not yet been officially awarded their PhD will be appointed at Research Assistant level

• The chance to work on cutting-edge research in low-latency and energy-efficient AI, tackling real challenges in sensor-to-GPU integration at the hardware-software boundary
• A highly active research environment within the Department of Electrical and Electronic Engineering at Imperial College London, with experts in machine learning, GPU programming, and digital hardware design
• Hands-on experience with algorithm-hardware co-design, including AI modelling, efficient ML methods, and GPU-based optimisation
• The opportunity to develop research publications and contribute to an…