Research Assistant / Research Associate – AI Model Optimisation for Edge Devices & NVIDIA Holoscan Sensor Bridge Integration

About the role

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://www.heronic.ai), 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.

What you would be doing

• 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
• 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

What we are looking for

Essential

• 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

Further information

This is a fixed-term position for up to 18 months (subject to probation), based in the Department of Electrical and Electronic Engineering at Imperial College London.

For questions about the role, please contact:
Professor Christos Bouganis – christos-savvas.bouganis@imperial.ac.uk

Please note that job descriptions are not exhaustive, and you may be asked to take on additional responsibilities aligned with those described above.

For technical issues with the online application, please email support.jobs@imperial.ac.uk

Closing date: 13/08/2026

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