Developing and Applying Semantic Representations of Scientific Knowledge

Applications are invited for a 3-year self-funded PhD Studentship

The depth, volume, and complexity of scientific knowledge is increasing beyond our abilities to meaningfully interpolate and analyse it with traditional methods. The majority of this knowledge is encoded in unstructured text or unlinked structured data. In this project, we will develop methods to create structured and linked representations of scientific knowledge, which encode their context and will thereby admit retrospective analysis, knowledge synthesis, and integration into the scientific method. Our applications once these representations are developed will be twofold: 1) we will develop provenance networks for the identification and analysis of misinformation and false facts and their propagation across scientific knowledgebases and 2) develop frameworks for inclusion of contextual background knowledge into research, e.g. priors for statistical analysis. Our application areas will focus on health data science, environmental data science, and radiation biology -areas in which we have on-going collaborations with domain experts.

Person Specification

Applicants should have a background in computer science, with strong grounding in data science and software engineering implementation skills. They should have an interest in natural language processing, knowledge graphs, biomedical ontologies, health and environment data, and algorithms development. They should have a commitment to research and hold or realistically expect to obtain at least an Upper Second Class Honours Degree in computer science, data science, or an adjacent discipline. 

How to apply

Applications should be directed to [email protected] To apply, please send:

●       A Detailed CV, including your nationality and country of birth;

●       Names and addresses of two referees;

●       A covering letter highlighting your research experience/capabilities;

●       Copies of your degree transcripts;

●       Evidence of your proficiency in the English language, if applicable.

Applicants will be required to attend an interview. This can be conducted face –to –face, by telephone or zoom