Post-doc in Machine Learning for Data from Political Science

How can new AI technology help us study the field of political science? What are the potential biases and pitfalls to avoid, and what are the strengths and weaknesses of different methods? We are looking for a post-doctoral researcher with an interest in machine learning applied to data from political science to work in a cross-disciplinary team. You will be part of a joint project with computer scientists at Chalmers and political scientists at Karlstad University.

Information about the project
This position is affiliated with the project "Bias and Methods of AI for studying Political Behviour", funded by the Wallenberg AI, Autonomous Systems and Software Programme on Humanities and Society (WASP-HS) : https://wasp-hs.org/projects/bias-and-methods-of-ai-technology-studying-political-behavior/.

In this project, we investigate AI methods for applications in political science research. We consider both best practice of current methods, as well as development of new methods suited to problems occurring in the political science domain. The project leaders are Dr. Annika Fredén (Karlstad, political science) and Dr. Moa Johansson (Chalmers, computer science). Currently, two PhD students are working in the project, one at each site. Our current research questions include for instance: detecting differences in language between different political actors and what that entails and analysis of political influencers in social media to study e.g. polarisation around controversial topics.

WASP-HS funds many related projects across Swedish universities, and we expect to also collaborate with some of these during the coming years.

Major responsibilities
The candidate is expected to conduct research related to the main theme of the project, in collaboration with the project leaders and PhD students already affiliated with the project. This may include investigating for instance which machine learning methods are most suitable for different kinds of data and tasks commonly dealt with by political sciences, as well as highlighting potential sources of bias arising from using different models, and different sized datasets. The results should be presented in such a way as to provide novel methodological guidelines and pedagogical examples to political scientists considering using AI and machine learning in their research. Other possible topics are bias detection and mitigation in machine learning and explainable AI.

The candidate is also strongly encouraged to formulate and suggest their own research questions related to the main project goals.

The position consists mainly of research, but also some teaching (10-20%) on undergraduate and MSc level, allowing the candidate to gain experience of both research and teaching, necessary for the next step in an academic career.

Position summary
Full-time temporary employment. The position is limited to a maximum of two years (1+1).

Qualifications
The candidate should have (or be about to finish) a PhD degree in a relevant field, including Artificial Intelligence, Computer Science, Data Science, Computational Social Science, Computational Linguistics, Mathematics or related fields. Furthermore, the candidate should have a strong interest in cross-disciplinary work and a willingness to learn more about political science and/or computer science, depending on background. The candidate should have some experience of machine learning.

Chalmers continuously strives to be an attractive employer. Equality and diversity are substantial foundations in all activities at Chalmers.

Our offer to you
Chalmers offers a cultivating and inspiring working environment in the dynamic city of Gothenburg. 
Read more about working at Chalmers and our benefits for employees.

PLEASE READ MORE AND APPLY HERE.

Application deadline: 31 August, 2021

For questions, please contact:
Dr. Moa Johansson, CSE, moa.johansson@chalmers.se

*** Chalmers declines to consider all offers of further announcement publishing or other types of support for the recruiting process in connection with this position. ***