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Research Fellow in Disease Progression Modelling and Machine Learning for Clinical Trials

UCL - Department of Computer Science

Location: London
Salary: £35,965 to £43,470 per annum, inclusive of London Allowance
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 26th March 2020
Closes: 26th April 2020
Job Ref: 1861361

The post is based at the Centre for Medical Image Computing (CMIC) within the UCL Department of Computer Science.

The main duties are:

• To extend and develop machine learning tools for individualised prediction and stratification in Alzheimer’s disease clinical trials.

• To incorporate disease mechanisms within these tools for informing future treatment development efforts.

The duration of this post will be 18 months in the first instance with a possibility to extend.

The applicant is required to have an honours degree (minimum 2:1) and a PhD in a discipline related to this project (mathematics, statistics, engineering, physics), and a track record in machine learning applied to large-scale data analysis. The applicant should also have excellent communication skills and a commitment to conducting high-quality research that will benefit healthcare.

We offer an attractive range of staff benefits including 41 days annual leave per year inclusive of closure days and bank holidays, pension scheme, season ticket loan, professional development, eye care scheme, discounted gym membership and much more.

All new staff joining will be appointed at the bottom increment of the pay grade, only in exceptional circumstances will the salary offered at a higher rate.

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. Neil Oxtoby ( ).

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.

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