Purpose of Role
The Theoretical and Empirical METaknowledge (TEMET) lab at Heriot-Watt is seeking a Research Assistant (RA) to help with data collection and other tasks, starting late February 2026 or earliest thereafter.
The RA will be working within the auspices of the pan-European WIDERA consortium project iRISE (Improving Reproducibility In SciencE) under the guidance of Dr Daniele Fanelli, within the School of Social Sciences at Heriot-Watt University.
The core task involves curating the data sources for the project, in particular helping to collect data and portions of text from samples of published studies and preparing them for subsequent analyses. Depending on the candidate’s interest and skills, they may also be directly involved in the development and testing of Natural Language Processing techniques and AI applications to analyse this data. Secondary tasks include helping with the organization of events, materials, and small administrative tasks related to the project.
Funds are available for up to 6 months full-time employment, but part-time employment may be considered as well.
Key Duties & Responsibilities
1. Help to identify and collect samples of scientific studies from suitable databases, collect meta-data of these articles, prepare texts extracted from them.
2. Contribute to the development of novel, transferable methodologies to analyse the scientific literature.
3. Help with and contribute to dissemination activities, such as conferences and workshops.
4. Help with general management of the project and research group (e.g. web page and social media communication).
Essential & Desirable Criteria
Essential
5. You should have a Master’s degree in a quantitative subject (any disciplinary background is accepted).
6. Some knowledge and experience conducting literature searches and data collection.
Desirable
7. Programming and statistical skills (especially R and/or Python).
8. Experience and/or a demonstrable interest in graphical methods of knowledge representation and/or quantitative text analysis and Natural Language Processing and/or AI.
9. Knowledge and practical understanding of basic concepts in information theory (e.g. Shannon Entropy, Kolmogorov complexity).