Sign up with your email address to be the first to know about new products, VIP offers, blog features & more.

hD studentship on NLP and statistical relational learning (University of Edinburgh), deadline: 1 Oct 2019

Applications are invited for a PhD studentship in natural language processing and statistical relational learning, to be based in the School of Informatics at the University of Edinburgh. The position is an opportunity to combine cutting-edge research at the intersection of language processing and machine learning.



Desired Background

The project is suitable for a student with a top MSc or first-class bachelor’s degree in machine learning, with a background in NLP and/or statistical relational learning. 


Previous coursework or experience (e.g., thesis) in one of these areas is necessary. A strong programming background will be essential for this project. Prior experience with knowledge graphs and relational embeddings  is highly desirable. 


Why Edinburgh

The School of Informatics at the University of Edinburgh has one of the largest concentrations of computer science research in Europe, with over 100 faculty members and 275 PhD students. The school is particularly strong in the research area of artificial intelligence. Our strength in these areas have been recognized by award of EPSRC Centre for Doctoral Training in Data Science. The University of Edinburgh is one of the founding partners of the Alan Turing Institute, the UK’s national research institute for data science.


Funding Information

The scholarship is available for UK/EU students and consists of an annual bursary up to a maximum of three years. Applicants should first contact Vaishak Belle (see below) to informally express their interest, and then 

apply through 


Application Information

For informal enquiries about the position, please contact Vaishak Belle: with a short summary of your background and research interests in the technical themes mentioned above. (No attachments please.) See the contact link for the prospective students section for pointers to relevant scientific papers.

Apply Time