Research Assistant/Associate – Cardiac MR Image Reconstruction/Analysis and Machine Learning
Health and Medical,Medical Technology,Computer Science,Computer Science,Artificial Intelligence
Short info about job
Company: Imperial College London
Department: Department of Computing
Salary: £32,380 to £44,220 per annum
Hours: Full Time
Contract type: Fixed-Term/Contract
Type / Role: Academic or Research
Phone: +44-1507 7083404
Fax: +44-1260 2502929
E-mail: N\A
Site: N\A
Detail information about job Research Assistant/Associate – Cardiac MR Image Reconstruction/Analysis and Machine Learning. Terms and conditions vacancy
Research Assistant salary range: £32,380 to £34,040 per annumResearch Associate salary range: £36,800 to £44,220 per annum
Fixed Term appointment for up to 48 months starting on 1st October 2017
The BioMedIA group is part of the Department of Computing which is a leading department of Computer Science among UK Universities. Imperial College has the greatest concentration of high impact research of any major UK university, according to the Research Excellence Framework (REF) 2014. The Department of Computing us the only Department in the UK that has been rated consistently amongst the top three since the introduction of research assessments. Imperial was also awarded “Gold” according the last Teaching Excellence Framework (TEF) 2017.
We are seeking to appoint a Research Assistant/Associate to develop novel machine learning algorithms for the reconstruction and analysis of cardiac MR images. The successful candidate will join the EPSRC funded SmartHeart (www.smart-heart.org) project, the next-generation of cardiovascular healthcare which uses integrated image acquisition, reconstruction, analysis and interpretation.
To apply, you will have a special interest in image analysis with particular emphasis on cardiac magnetic resonance imaging (MRI). You will work with a wider community of computer scientists, cardiologist and medical imaging experts from leading hospitals and UK Biobank to develop medical image reconstruction and segmentation algorithms. You should be willing to get involved at all levels from image reconstruction and image analysis to clinical translation.
At Research Assistant level you will need to have a first-class undergraduate degree (or equivalent) in a relevant discipline with a particular interest in machine learning and computer vision. Preference will be given to applicants with a proven track record in medical imaging. To be appointed a Research Associate level you must have been awarded a PhD (or equivalent) in a subject relevant to medical imaging with particular expertise in medical image reconstruction, medical image computing, computer vision or machine learning.
All applicants must be fluent in spoken and written English. You must have excellent communication skills and be able to organise your own work with minimal supervision and prioritise work to meet deadlines. You will be part of the Biomedical Image Analysis Group (BioMedIA) based at the South Kensington campus in London. The mission of the group is to develop novel, computational techniques for the analysis of biomedical images. For further information about the group and related projects see: http://biomedic.doc.ic.ac.uk/.
How to apply:Our preferred method of application is online via our website at: http://www3.imperial.ac.uk/employment
Please select “job search” then enter the job title or vacancy reference number EN20170297SA into “keywords”. Please complete and upload an application form as directed.
Applications must include the following:
- A college application form
- A full CV
- A two-page research statement indicating what you see are interesting research issues relating to the above post and why your expertise is relevant.
- Any element relating your experience / passion for software engineering (blog, open source projects, github repositories and others) will be carefully inspected.
Closing Date: 2 October 2017 (midnight BST)
Committed to equality and valuing diversity. We are also an Athena SWAN Silver Award winner, a Stonewall Diversity Champion, a Two Ticks Employer, and are working in partnership with GIRES to promote respect for trans people.