Research Associate in Deep Learning for Computational Lightfield Microscopy

at  Imperial College London

AVAILABLE DOCUMENTS

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• Download: Employee Benefits Booklet.Pdf
• Download: Job Description Research Assistant Or Associate ENG03276.Pdf

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ABOUT THE ROLE

Applications are invited for the above post to work with Prof. Pier Luigi Dragotti 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 Prof. Pier Luigi Dragotti who is an expert in machine learning, signal processing and computational imaging (Imperial, EEE), Prof. Christos Bourganis (Imperial, EEE); and Dr. Samuel Barnes (Imperial, Brain Sciences).
The successful candidate will develop a new generation of computationally efficient, stable, and interpretable deep neural networks for volume reconstruction from lightfield video sequences produced by our lightfield microscope.

WHAT YOU WOULD BE DOING

You will implement, test and optimize new model-based deep neural networks (DNN) for real-time, neural activity extraction from lightfield microscopy data. You will develop strategies to systematically embed prior knowledge and constraints about neural signals and image acquisition optics into the DNN architectures. Your neural networks will be robust to distribution shifts and will be trained in a semi-supervised fashion using small amount of training data. You will also work with postdoctoral associates in EEE and bioengineering to develop algorithms that will be implemented in a field-programmable gate array for real-time readout