This is a fully funded 4-year full-time PhD position, which will be based at the School
of Computer Science in University College Dublin, commencing in September 2021
Description: What is the role of migration in the dynamics of cultural development as
regards gender, ethnicity, and demography? To answer this question, the EU-funded
VICTEUR project will work with the SFI-funded Insight Centre for Data Analytics at
UCD to explore large-scale historical text corpora from a computational perspective.
This will allow us to study intra-European migration during the 19th century through
the ‘macroscope’ of machine learning and the ‘microscope’ of literary scholarship.
As part of this research project, the successful candidate for this PhD role will work on
developing new techniques that use text mining and natural language processing to
extract insights and trends from large collections of historical texts, combining
automated content analysis and the integration of “human-in-the-loop” expertise. The
PhD project will also look at the application of these models to textual data coming
from other sources and domains.
BSc or MSc Degree in Computer Science or another relevant technical discipline.
Prior knowledge in machine learning, natural language processing, text mining or
Excellent programming and technical skills.
Strong written and verbal communication skills.
Excellent organisational skills, including a proven ability to work to deadlines.
Evidence of prior research activity.
Experience of collaborating with researchers from other disciplines.
Programming experience working with the Python Data Science Stack.
Send the following by 10th June 2021 to Dr. Derek Greene firstname.lastname@example.org
A recent Curriculum Vitae.
A personal letter of motivation explaining your background and why you wish to apply
for this research project.
Evidence of recent technical work, if available (e.g., link to a personal GitHub
This role has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme Grant agreement No. 884951.