Research Associate in image registration and machine learning for interpretable neuroscience

London (Greater) (GB)
Grade 6, Salary £38,304 - £45,026, including London Weighting Allowance
Sep 09, 2020
End of advertisement period
Oct 07, 2020
Academic Discipline
Life sciences, Biological Sciences
Contract Type
Fixed Term
Full Time

This is an exciting opportunity for a software engineer or research associate interested in learning about machine learning and deep learning to join a collaborative project shared between King’s College London, the FMRIB centre, University of Oxford, and the Donders Institute, Nijmegen.

The post-holder will be responsible for developing and implementing novel algorithms and pipelines for surface mesh modelling; in particular, extending work on brain correspondence matching (, using machine learning and/or discrete optimisation in order to improve the accuracy with which brain scans may be compared across individuals.

The overall aim of the award is to improve the translation of imaging into clinical practice by improving the precision with which imaging reflects biological processes, as well as designing more sensitive, interpretable, scalable, machine learning models for population analysis (and personalised trait prediction) in Big Data cohorts such as UK Biobank.

The project is jointly overseen by Dr Emma Robinson (KCL and Professors Saad Jbabdi, Steve Smith, Mark Jenkinson, Mark Woolrich, Karla Miller (Oxford), and Christian Beckmann (Nijmegen). Successful applicants will work under the supervision of two or more of the above, and in close collaboration with all the PIs on the grant

The successful candidate will have a graduate degree in computer science or a closely related field. They will be able to demonstrate good software development skills with significant experience in C++ and Python, version control, software package release, support and management. In addition, we would look for experience in at least one of the following skills: machine learning, numerical optimisation, graphics or accelerated programming. Training in the relevant domains will also be provided through departmental courses, as well as collaborations with other developers and researchers.

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