Research Scientist in Deep Learning for Computational Imaging

3 weeks ago

London, Greater London, United Kingdom Imperial College London Full time
Job Opportunity: Research Associate in Deep Learning for Computational Lightfield Microscopy

We are seeking a highly skilled Research Associate to join our team at Imperial College London, working with Prof. Pier Luigi Dragotti and his team on a project called "Optical Oscilloscope: Real-time, High-throughput, Volumetric Voltage Imaging."

The successful candidate will be integral to delivering on this project, which aims 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, an expert in machine learning, signal processing, and computational imaging (Imperial, EEE), Prof. Christos Bourganis (Imperial, EEE), and Dr. Samuel Barnes (Imperial, Brain Sciences).

Key Responsibilities:
  • Develop new generation of computationally efficient, stable, and interpretable deep neural networks for volume reconstruction from lightfield video sequences produced by our lightfield microscope.
  • Implement, test, and optimize new model-based deep neural networks (DNN) for real-time, neural activity extraction from lightfield microscopy data.
  • Develop strategies to systematically embed prior knowledge and constraints about neural signals and image acquisition optics into the DNN architectures.
  • Train neural networks in a semi-supervised fashion using small amounts of training data.
  • 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.
Requirements:
  • PhD (or near completion) in engineering, mathematics, physics, or a related field.
  • Experience with developing model-based deep learning architectures and algorithms for inverse imaging problems.
  • Good publication record, ability to work well in teams, and ability to work to tight deadlines.
  • Preference given to candidates with a strong background in signal/image processing and experience with programming in Matlab, Python, and/or C/C++.
What We Offer:
  • Opportunity to form a key part of the multidisciplinary team innovating 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 working on imaging problems, deep learning, and high-dimensional 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 inaccessible data.
  • Diverse and supportive training environment promoting transdisciplinary fluency.
  • Further professional training and network development provided.
  • Opportunity to continue your career at a world-leading institution.
  • Sector-leading salary and remuneration package.

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