Research Associate in Computational Analysis of Video-Telemetry
We are seeking a candidate to develop novel artificial intelligence algorithms to analyse video-telemetry data obtained from patients with epilepsy. Video-telemetry data comprises simultaneous recording of video and EEG data, which is then reviewed by trained neurologists to infer brain regions involved in epileptic seizures. The development of automated algorithms could help to characterise patient motion during seizure, classify seizures into different subtypes, and predict brain regions involved in seizure onset. Automated analysis of these videos could assist neurologist in determining the appropriate treatment for patients with epilepsy.
In this role, the successful candidate will be responsible for developing novel algorithms to characterise patient motion from video data and using this information to classify seizure subtype and predict brain region involvement. The successful candidate is expected to disseminate their research through presenting at scientific conferences, such as MICCAI, MIDL, or ICCV, and via scientific publications in peer-reviewed journals.
Candidates must have a PhD in image processing, medical image analysis, computer vision, machine learning or a closely related field. Candidates should have experience working on at least one of the following image analysis research topics: classification, segmentation.
Candidates are expected to have demonstratable experience in designing neural networks or machine learning algorithms applied to either medical images, natural images, or video data. Experience with PyTorch, TensorFlow, or equivalent software packages is required. Familiarity with version control software (git, svn) and experience working within a multi-developer team is desirable.
Candidates are expected to have a good track record of scientific publications in computer vision or medical imaging journals or equivalent conference publications. Candidates should have strong written and oral presentation skills with experience presenting to technical audiences.
Candidates will be based in the Department of Surgical and Interventional Engineering reporting to Dr. Rachel Sparks, a Lecturer within the department.