provides the opportunity to work in the vibrant and highly collegial research environment for which the SMG is renowned.
The successful candidate will be in charge of the Work Package focusing on New Models, which aims to enable precise and comparable measures of morphological predictability, in order to understand how predictability is organized in realistic, complex inflectional systems.
As a core member of the team, the successful candidate will use their skills in software development to extend existing software (Qumin) which analyzes morphological lexical data and evaluates inflectional predictability. They will rely on their qualifications in linguistics to study predictability in systems which challenge existing tools and analyses, pushing the edge of what can be reliably measured and contribute to the large comparative study of macro-variation in predictability, gaining experience in linguistic typology. They will collaborate closely with the team in the writing and dissemination of papers and presentations, and be expected to take initiative in the formulation of the research agenda.
About you
The successful candidate will have a dual background in linguistics and software development, with a PhD in computational linguistics, natural language processing, or a related field. The candidate will have a good grasp of linguistic concepts and methods. They will have an interest in the inflectional morphology of at least one language or language family, which can serve as a test-bed for the tools they will develop. They will have experience of software development, and are fluent in Python programming. Candidates with experience programming as part of a team will be preferred. Mastery over software development tools (command line; git management; continuous pipelines and workflows; IDE) is essential. An interest in reproducibility in science will be an advantage. Experience in using these skills to answer linguistic questions is essential.
How to apply
Please submit your application online:
- CV
- Contact details for 2 academic referees.
- Three relevant works of research (DOI/URL links if possible).
- Research statement of one page, describing research interests and experience, and how you think this makes you suitable for the advertised post.
Please do not seek assistance from AI tools in writing this document.
Interviews are planned for Tuesday the 24th of November and will be held online. Please contact Sacha Beniamine, s.beniamine@surrey.ac.uk with any questions.
Further details
Job Description
