About the role
We are now inviting applications for a post-doctoral training Fellowship under the guidance of Professor Peter Latham.This role lies at the interface of deep learning theory, linguistics, and theoretical neuroscience. The position will focus on developing theoretical approaches to investigating how and when modularity arises in language related models. The research will borrow heavily from techniques in theoretical neuroscience, deep learning, machine learning and physics and applicants should have expertise in several of these areas.
You will be responsible for the primary execution of the project (with opportunities for co-supervision of students), presentation of results at conferences and seminars, and publication in suitable media.
This post is initially funded for 2 years with the possibility of a one-year extension at the end of the period.
A job description and person specification can be accessed at the bottom of this page.
About you
You should have a strong quantitative background in theoretical neuroscience, machine learning, statistics, computer science, physics or engineering; a record of publication in highly respected journals and conferences and must hold a PhD in a relevant field by the agreed start date of the position.
To apply, please click Apply Now and submit your CV which should include names of 3 referees; and a statement covering your research accomplishments. There is no requirement to upload any papers you have authored.
What we offer
The Gatsby Unit offers competitive salaries and an award-winning work environment. Salary details are in the job description document.
You will work in a vibrant, interactive and collaborative environment, with world-class PhD programmes, generous core funding andvtravel allowances. Our facilities include an on-site high-performance computer platform, an extensive seminar programme and interaction space, an on-site brasserie, and outdoor spaces. Our staff are entitled to UCL's extensive range of staff benefits, including a generous annual leave entitlement, family-friendly policies, occupational pension schemes, relocation and housing assistance (where applicable) and professional development opportunities.
