Research Associate or Research Fellow: AI-enabled Neurology
UCL and KCL are launching a £4.5 million, Wellcome-funded, four-year programme of translational research seeking to transform acute neurology through the application of novel, domain-tailored machine learning.
The successful applicants will be responsible for developing and integrating new models and tools for clinical and imaging data analysis and reporting, including modelling of clinical and operational outcomes for acute neurological applications.
The programme will be using infrastructure co-developed as part of the new London Medical Imaging and AI centre for Value Based Healthcare, which is creating a general-purpose open-source AI infrastructure based on technologies such as XNAT, CogStack and ElasticSearch, and NiftyNet. The applicant is expected to use, work with, and contribute towards these software packages as part of their activities.
The applicant should ideally have some knowledge and experience of Medical image analysis, machine learning, data science and statistical modelling.
The position would appeal to a candidate with strong software development skills, including: Classic machine learning and deep learning packages (SciKitLearn, Tensorflow, pytorch,…), data science and statistical modelling packages (Pandas, SciPy/Statsmodels) and database querying tools and APIs (REST, MySQL, ElasticSearch/Lucene).
An interest in neurological and neuroradiological applications is important, but previous experience is not essential. Previous experience with large-scale computing services would be useful but not essential.