Research Associate in Deep Learning for Computational Lightfield Microscopy
Job Description
Faculty / Department
Faculty of Engineering
Salary
£48,056 – £56,345 per annum
Location / Campus
South Kensington Campus – Hybrid
Contract Type / Work Pattern
Full time – Fixed term
Closing date
20-Oct-2024
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
What we are looking for
The successful candidate:
Will have a PhD (or be very near completion, or equivalent) in engineering, mathematics, physics, or a related topic.
Experience with developing model-based deep learning architectures and with developing algorithms for inverse imaging problems is required.
The successful candidate will have a good publication record, show evidence of working well in teams, and demonstrate an ability to work to tight deadlines.
Preference will be given to candidates with a strong background in signal/image processing and to those with experience with programming in Matlab, Python and/or C/C++.
The ideal candidate will be both a “tool developer” and a “tool user” with a keen interest in imaging and inverse problems.
What we can offer you
The opportunity to form a key part of the multidisciplinary team who will innovate methods for high-throughput, volumetric voltage imaging
Training in mathematical methods and algorithms for inverse problems and close interaction with the highly motivated and diverse team of Prof. Dragotti all working on imaging problems, deep learning and high-dimentional data analysis
First access to spatially-resolved cortical microcircuit voltage data acquired on a previously inaccessible scale. Flexibility to develop your own algorithms and research vision based on this previously unaccessible data
A diverse and supportive training environment promoting transdisciplinary fluency
Further professional training and network development provided by Imperial’s Postdoctoral and Fellows Development Centre
The opportunity to continue your career at a world-leading institution
Sector-leading salary and remuneration package
Further information
Professor Dragotti’s team is based at Imperial College’s South Kensington Campus in the heart of London, UK. Prof. Dragotti is an expert in mathematical methods for image/signal processing and he applies these methods to develop interpretable model based neural networks for computational imaging applications. Prof. Dragotti leads a very diverse research team working on many interdisciplinary projects. He provides a diverse and supportive training environment promoting interdisciplinary research.
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: Pier Luigi Dragotti [email protected].
Available documents
Attached documents are available under links. Clicking a document link will initialize its download.
Download: Employee Benefits Booklet.Pdf
Download: Job Description Research Assistant Or Associate ENG03276.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 [email protected]. We’re here to help.
About Imperial
Welcome to Imperial, a global top ten university where scientific imagination leads to world-changing impact.
Join us and be part of something bigger. From global health to climate change, AI to business leadership, here at Imperial we navigate some of the world’s toughest challenges. Whatever your role, your contribution will have a lasting impact.
As a member of our vibrant community of 22,000 students and 8,000 staff, you’ll collaborate with passionate minds across nine London campuses and a global network.
This is your chance to help shape the future. We hope you’ll join us at Imperial College London.
Our Culture
We work towards equality of opportunity, to eliminating discrimination, and to creating an inclusive working environment for all. We encourage applications from all backgrounds, communities and industries, and are committed to employing a team that has diverse skills, experiences and abilities. You can read more about our commitment on our webpages.
Our values are at the root of everything we do and everyone in our community is expected to demonstrate respect, collaboration, excellence, integrity, and innovation.