Research Assistant/Associate in Machine learning for Cardiac MR and US Image Analysis Imperial College London, Department of Computing

Research Assistant/Associate – Machine learning for Cardiac MR and US Image Analysis


Job summary


The successful candidate will join an exiciting EPSRC funded research project which is a collaboration between the BioMedIA group at Imperial College London and King’s College London. The aim of project is to develop deep learning techniques that in combination with rich multimodal imaging data can be used to build a predictive model of heart disease and its associations with heart shape and motion. The BioMedIA group is part of the...

 

Job listing information


Reference ENG00117


Date posted 14 December 2017


Closing date 15 January 2018


Key information about the role


Location
South Kensington Campus
(map)



Position type
Full time, fixed term


Salary
£32,380 – £40,840 plus benefits Department Department of Computing Category Researcher / Non Clinical Researcher


Job description


Job summary


The successful candidate will join an exiciting EPSRC funded research project which is a collaboration between the BioMedIA group at Imperial College London and King’s College London. The aim of project is to develop deep learning techniques that in combination with rich multimodal imaging data can be used to build a predictive model of heart disease and its associations with heart shape and motion.


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.

 

Duties and responsibilities


We are seeking to appoint a Research Assistant/Associate to develop novel machine learning algorithms for the analysis of cardiac MR and US images. 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.

 

Essential requirements


To apply, you will have a special interest in image analysis with particular emphasis on cardiac magnetic resonance imaging (MRI). 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/.

 

Further information


Applications must include the following:


• 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.

 

Should you have any queries regarding the application process please contact Georgina Tennant by email to: g.tennant@imperial.ac.uk

 

For technical issues when applying online, please contact: erecruitment@imperial.ac.uk

 

Documents

  • Research Assistant Associate JD_P66527.DOC

 

About Imperial College London


Imperial College London is the UK’s only university focussed entirely on science, engineering, medicine and business and we are consistently rated in the top 10 universities in the world.

 

You will find our main London campus in South Kensington, with our hospital campuses located nearby in West and North London. We also have Silwood Park in Berkshire and state-of-the-art facilities in development at our major new campus in White City.

 

We work in a multidisciplinary and diverse community for education, research, translation and commercialisation, harnessing science and innovation to tackle the big global challenges our complex world faces.

 

It’s our mission to achieve enduring excellence in all that we do for the benefit of society – and we are looking for the most talented people to help us get there.

 

Additional information


Please note that job descriptions cannot be exhaustive and the post-holder may be required to undertake other duties, which are broadly in line with the above key responsibilities.

 

All Imperial employees are expected to follow the 7 principles of Imperial Expectations:

  • Champion a positive approach to change and opportunity
  • Communicate regularly and effectively within, and across, teams
  • Consider the thoughts and expectations of others
  • Deliver positive outcomes
  • Encourage inclusive participation and eliminate discrimination
  • Develop and grow skills and expertise
  • Work in a planned and managed way 

 

In addition to the above, employees are required to observe and comply with all College policies and regulations.

 

Imperial College is committed to equality of opportunity, to eliminating discrimination and to creating an inclusive working environment. We are an Athena SWAN Silver award winner, a Stonewall Diversity Champion, a Disability Confident Employer and work in partnership with GIRES to promote respect for trans people.


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