|Salary:||£36,028 to £41,228 Per annum, inclusive of London Allowance.|
|Placed On:||13th January 2021|
|Closes:||26th January 2021|
The post-holder will have the opportunity to join the UCL Centre for Medical Image Computing (CMIC) to apply deep learning to neuroimaging data to better understand patterns of brain development.
The role involves large-scale data analysis of multi-modality magnetic resonance imaging data, particularly working with the data collated by the Bill & Melinda Gates Foundation. The post holder will take the lead in applying advanced statistical techniques, writing reports and manuscripts for publication, and presenting the study findings at meetings and conferences.
Duration of the post is funded for up to 15 months in the first instance, grant expires 31/05/2022.
The post holder is expected to have completed (or be close to completing) a PhD that involves applying advanced statistical methods to neuroimaging data. Previous deep learning analysis will be very important. Experience with neuroimaging data from children or adolescents is highly beneficial. Key skills include familiarity with command-line interface computing and statistical/data science programming languages (e.g., R, Python, Matlab).
Appointment at Grade 7 is dependent upon having been awarded a PhD; if this is not the case, initial appointment will be at research assistant Grade 6B (salary £31,542 - £33,257 per annum) with payment at Grade 7 being backdated to the date of final submission of the PhD thesis.
Applicants should apply online. To access further details about the position and how to apply please click on the ‘Apply’ button above.
If you have any queries regarding the vacancy or the application process, please contact Dr James Cole (email email@example.com).
Latest time for the submission of applications: 23:59.
Interview Date: TBC
UCL Taking Action for Equality
We will consider applications to work on a part-time, flexible and job share basis wherever possible.
Our department holds an Athena SWAN Silver award, in recognition of our commitment and demonstrable impact in advancing gender equality.
Type / Role: