Job summary
We are seeking either a Research Assistant or Research Associate to work in this project. The post is funded by the EU H2020 CHAIMELEON project and aims to demonstrate for first time that AI can be used to enhance reproducibility of radiomics features and parameters extracted from cross-vendor and cross-institution CT-MR-PET/MR imaging data.

Increasing favourable outcomes suggests that health imaging-based AI approaches can become useful clinical tools in areas such as non-invasive tumour characterisation, prediction of certain tumour features, staging of tumour spread, stratification of patients, selection of most appropriate therapies and clinical prognosis.

Duties and responsibilities
The main contribution of the group led by Dr Guang Yang to CHAIMELEON project focuses on the investigation and development of novel data quality enhancement and harmonisation, and federated machine learning algorithms for cross-vendor and cross-institution AI powered data repository construction, including the investigation of new strategies in medical imaging acquisition, reconstruction, as well as novel mechanisms to generate adversarial examples and mitigate their effects. The work also includes the analysis of scenarios where data privacy can be enhanced for large multimodal clinical data repository.

There will be opportunities to collaborate with other researchers and PhD students in the CHAIMELEON consortium, which includes 18 top-tier UK/EU research institutes and high-tech companies.

Essential requirements
Applicants must demonstrate as part of their application, how they meet the essential criteria required for the post. To be appointed as a Research Assistant you should have or be close to completion of a PhD degree (or equivalent) in an area pertinent to the subject area, i.e., Computing or Engineering, for the Research Associate position, or a good first degree in related area for the Research Assistant position. You must have excellent verbal and written communication skills, enjoy working in collaboratively and be able to organise your own work with minimal supervision and prioritise work to meet deadlines. Preference will be given to applicants with a proven research record and publications in the relevant areas, including in prestigious machine learning, computer vision and medical image analysis journals and conferences.

In particular, Research Associate applicants must hold a PhD in a relevant discipline and all applicants should have equivalent laboratory experience. In addition, you will need to have a strong machine learning background with proven knowledge and track record in one or more of the following research areas and techniques: generative adversarial models, federated or distributed machine learning, deep learning and its applications to medical image reconstruction, denoising and data harmonisation.

Further information
The post is full time and fixed term for up to 36 months.

Candidates who have not yet been officially awarded their PhD will be appointed as a Research Assistant within the salary range £35,477 - £38,566 per annum.

Should you require any further details on the role please contact: Dr Guang Yang – g.yang@imperial.ac.uk.

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