Prof. Dr. Robert Fuchs

World Englishes/postcolonial varieties of Englishes, corpus linguistics, gender and language, sociolinguistics, second language acquisition (esp. speech learning, tense and aspect), corpus-based discourse analysis, social media, acoustic phonetics and laboratory phonology.

The questions I investigate in my research include:

• Artificial Intelligence and language: How can Large Language Models (LLMs), and Artificial Intelligence more generally, be used in linguistic analysis? How do LLMs influence language use and language change, as well as professions such as language teaching, writing, and journalism?
• Recent language change: How has the English language changed in the recent past and how is it likely to change in the near future? Who are the most innovative or progressive speakers in ongoing language change and why are these particular groups the most progressive? Does English change in similar ways in different places around the world?
• How does English vary structurally across age and gender groups?
• How does the context of the use of English and the structure of local varieties of English vary around the world?
• What consequences does the increasing use of English in many countries around the world have in terms of language attitudes?
• How is English learnt by speakers of diverse first languages and what structures of the English language are particularly easy or hard to learn?
• How is public discourse on sensitive topics structured and how does it affect real-world outcomes? (e.g. during the COVID-19 pandemic)

We occasionally advertise Research Associate positions for prospective PhD students that involve participation in administration and teaching, or an externally funded research project. These positions are advertised by the university as they become available.

In addition, I can accept a limited number of self-funded PhD students (e.g. through scholarships). Applicants should submit (i) a representative piece of writing (e.g. MA thesis), (ii) a research proposal (two to five pages) and (iii) a CV (no more than three pages). Proposals need to closely align with the current research themes in our working group (see our current research: https://www.iaak.uni-bonn.de/bael/en/research/research-projects and publications: https://www.iaak.uni-bonn.de/bael/en/people/chair/publications). Proposals can also present several options. What we are looking for is evidence of excellent research skills, i.e. in terms of academic writing, empirical work and data analysis, corpus linguistics and data science. Applicants should also briefly describe their experience with R, Python and relevant data science methods (e.g. mixed effects models) and, where possible, provide links to representative code, notebooks or repositories.

Applicants who do not yet have substantial training in quantitative and computational methods are encouraged to complete relevant self-study before submitting or revising a proposal. Evidence of skills in this area is a requirement for PhD applicants. A suitable pathway would include the LADAL courses and tutorials on R, statistics, regression, mixed-effects modelling, and corpus linguistics, supplemented by R for Data Science and Bodo Winter’s Statistics for Linguists. Applicants proposing NLP or machine-learning methods should additionally acquire practical Python experience through the University of Helsinki’s Applied Language Technology courses and the Machine Learning in Python with scikit-learn MOOC. Completion certificates alone are not sufficient evidence: applicants should ideally provide a small reproducible analysis demonstrating data preparation, visualisation, appropriate statistical modelling, diagnostics, interpretation, and clear documentation of the code and workflow.