Collaborative research projects

Members of the research group are open to receiving enquiries about collaborative research projects from fellow academics at other departments within the University of Glasgow, or from academics at other academic institutions, and from individuals working at non-academic institutions (e.g. organisations in the private and public sector, as well as non-for-profit organisations).

 

Both academic and non-academic partners might want to browse the profiles of the group’s members to explore their different research interests and their most recent research work. In addition, non-academic partners might want to read about the impact of the group’s research beyond academia.

 

In addition to externally-funded research projects, members of the research group also welcome enquiries from non-academic institutions to develop collaborative LLM dissertation projects. A collaborative dissertation is a research dissertation conducted between a student and an external partner organisation, with academic guidance and support coming from an academic supervisor. For example, in recent years, our LLM students have written their dissertations in collaboration with international private law firms and international NGOs to support these organisations in the exploration of topics such as the legal obligations of private and public actors to take into account the impact of their operations on the current climate emergency. The University of Glasgow’s Collaborative Dissertation Initiative is based on an awareness that many organisations need good quality research and that students are often eager to conduct research, which has immediate relevance to external organisations and demonstrates the application of their academic learning.

 

For general enquiries about collaborative LLM dissertations, please contact Dr Javier Solana. To discuss the details of a specific collaborative research project, including collaborative LLM dissertations, please contact the relevant member of the group whose research you find most relevant.