Research Associate in Deep Learning for Computational Lightfield Microscopy

at  Imperial College London

FURTHER INFORMATION

Professor Bouganis’ team is based at Imperial College’s South Kensington campus in the heart of London, UK. Professor Bouganis is an expert in the design of intelligent digital systems and leads the iDSL group at Imperial College (https://www.imperial.ac.uk/idsl), with a focus on the theory and practice of reconfigurable computing and design automation, mainly targeting the domains of Machine Learning, Computer Vision and Robotics. Professor Bouganis leads a very diverse research team working on many interdisciplinary projects.
This full-time, in-person postdoctoral position is based at Imperial College’s South Kensington campus in London, UK and is funded for 18 months initially, starting in November 2024. In October 2025, we will compete for the Wellcome Trust scale-up phase, which could extend the project’s funding for an additional 6.5 years.
If you require any further details on the role please contact: 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 duties that align with the key responsibilities mentioned above.
If you encounter any technical issues while applying online, please don’t hesitate to email us at support.jobs@imperial.ac.uk. We’re here to help.

AVAILABLE DOCUMENTS

Attached documents are available under links. Clicking a document link will initialize its download.

• Download: Research Assistant Or Associate Job Description.Pdf

Please note that job descriptions are not exhaustive, and you may be asked to take on additional duties that align with the key responsibilities mentioned above.
We reserve the right to close the advert prior to the closing date stated should we receive a high volume of applications. It is therefore advisable that you submit your application as early as possible to avoid disappointment.
If you encounter any technical issues while applying online, please don’t hesitate to email us at support.jobs@imperial.ac.uk. We’re here to help.

ABOUT THE ROLE

Applications are invited for the above post to work with Professor Christos Bouganis and his team at Imperial College London for a Wellcome Trust funded project.
The successful candidate will be integral to delivering on the project called “Optical Oscilloscope: Real-time, High-throughput, Volumetric Voltage Imaging.” Our goal is to enable real-time, kilohertz, volumetric voltage imaging in 1,000 cells simultaneously within scattering mammalian brain tissue. The project is driven by a transdisciplinary consortium led by Dr Foust (Imperial, Bioengineering), and includes Professor Pier Luigi Dragotti who is an expert in machine learning, signal processing and computational imaging (Imperial, EEE), Professor Christos Bouganis (Imperial, EEE); and Dr Samuel Barnes (Imperial, Brain Sciences).
The successful candidate will research and develop a computational platform based on GPU and FPGA devices that would accelerate the execution of deep neural networks designed by the rest of the team for volume reconstruction from lightfield video sequences produced by our lightfield microscope.

WHAT YOU WOULD BE DOING

You will design, build and optimize a platform based on GPUs and FPGAs that aims to accelerate the computation of new model-based deep neural networks (DNN) for real-time neural activity extraction from lightfield microscopy data. You will research and build the infrastructure that would enable the communication of the CPU-GPU-FPGA subsystems, in order to achieve low-latency and high-throughput. You will investigate and implement strategies for the mapping of the DNN architectures for volume reconstruction developed by the rest of the team to the available computational platforms in order to minimise the latency of the computation. You will also consider the optimisation of the DNN models by investigating different quantisation methods taking into account the available hardware resources.
You will also work with postdoctoral associates in EEE and Bioengineering to develop algorithms for volume reconstruction that can be efficiently mapped into hardware.