5 months ago
A unique opportunity has opened for a Deep Learning researcher with a PhD in image analysis to join an innovative company who are pioneering advancements in healthcare by utilising state of the art Artificial Intelligence.
You will be developing scalable and robust Artificial Intelligence and Deep Learning techniques for the analysis and interpretation of specific medical images.
This novel work will be a major redesign of clinical methodologies and will have a genuine impact on the way we diagnose and prevent specific medical issues.
Duties and responsibilities
Interpretable and explainable AI/DL systems for the interpretation of medical images.
Transfer learning and verification for robust AI/DL systems that can be deployed in clinical practice.
Scalable deep learning expertise for large-scale databases of medical images.
Multi-modal deep learning algorithms for medical images and related clinical data.
A PhD in an area pertinent to the subject area, i.e. Computing, Maths or Engineering.
A strong artificial intelligence or deep learning background, specifically for image analysis (Ideally CT scans but MRI knowledge also advantageous)
Proven knowledge and track record in several of the following areas: Deep learning, computer vision and medical image analysis (in particular segmentation, registration, feature analysis, modelling or visualization).
A strong publication track record in relevant conference and/or journals.
Practical experience within a multi-disciplinary research environment.
Excellent programming skills.
Excellent oral and written communication skills.
Able to organise your work with minimal supervision and prioritise work to meet deadlines.
This position is based in a great location in Oxford which is easily accessible from: Abingdon, Didcot, Reading, Newbury and High Wycombe
If this position sounds of interest please apply directly to this advert of get in touch with Michael Winter
Michael.email@example.com 01189559530 07825711262
Applications are encouraged from all candidates meeting or exceeding the minimum criteria for the role regardless of age, disability, gender, orientation, race, religion or ethnicity.