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Postdoctoral Research Associate in Deep Learning for Enhanced Prediction of Neonatal Developmental Outcome from Multi-Modality MRI

King's College London - Department of Biomedical Engineering

Location: London
Salary: £37,412 to £39,484 per annum, inclusive of £3,223 per annum London Allowance (Grade 6)
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 21st November 2018
Closes: 6th January 2019
Job Ref: R6/MRE/2348/18-PO
 

Closing date: Midnight on 6th January 2019

Salary: The salary will be paid at Grade 6, £37,412 to £39,484 per annum, inclusive of £3,223 per annum London Allowance.

The Department of Biomedical Engineering, King’s College London seeks a Research Associate to work with Dr Emma C. Robinson on the development of novel Deep Learning methodologies for investigation of how heterogeneity in cortical organisation relates to behavioural and cognitive diversity. The project targets challenging questions relating to the impact of preterm birth on neurological development and builds on research that has been published in high impact journals including Nature and eLife.

Employment would ideally start in Spring/Summer 2019 and is funded by the Academy of Medical Sciences for a period of 12 months. During this time, the post-holder will contribute to:

  • Joint modelling of multi-modality MRI data sampled from the (Developing) Human Connectome Projects
  • Development of Deep Learning techniques for analysis of cortical surface mesh data using graph convolutions
  • Visualisation and interpretation of network features.

Candidates should have a doctorate in a relevant discipline, demonstrating a background in machine/deep learning and Python/C++ programming. Previous experience in working with neuroimaging data is not essential; however, expertise in working with spatio-temporal data is highly desirable given a central focus will be the development of tools that analyse functional MRI data.

Contract type: This post will be offered on a fixed-term contract for 12 months.

Contact: Dr Emma Robinson emma.robinson@kcl.ac.uk 

Application details: To apply, please register with the King’s College London application portal and complete your application online.

   
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