Application of Large Language Models for Systematic Review Automation

Fondazione Bruno Kessler (FBK) is a leading research and innovation organization based in Trento, Italy. With over 450 researchers across 12 Research Centers, FBK operates in diverse scientific and technological fields, organized into two main clusters: Technology & Innovation and Humanities & Social Sciences. More information: .
Within this context, the Mo DiS (M Otivational D Igital Systems) Research Unit is part of the Digital Society Center and focuses on the development of methodologies and technologies that promote user engagement, motivation, and behavior change. Particular attention is given to game-based and persuasive techniques that enable the personalization of digital experiences, applied across domains such as education, sustainable mobility, and waste reduction. More information:
The Digital Society Center conducts interdisciplinary research and develops digital technologies to address challenges in areas such as integrative AI, intelligence at the edge, and socio-technical systems. The center fosters collaborations with public administrations, industry partners, and academic institutions to support the digital and green transition and to build a more sustainable society.
Planned Activities
The Mo DiS Research Unit is seeking a motivated intern with a strong interest in Artificial Intelligence and its application to scientific research workflows. The internship will focus on the automation of systematic reviews using Large Language Models (LL Ms), supporting tasks from literature screening to qualitative coding. Working closely with Mo DiS researchers, the intern will contribute to the following activities:

Analysis of the systematic review process to identify stages suitable for automation;
Development of prompt engineering strategies and/or fine-tuning approaches;
Evaluation of model performance and comparison with traditional/manual review processes;
Design and implementation of pipelines integrating LL Ms (e. g. , GPT-based tools) for study screening and data extraction;
The internship will offer the opportunity to work in a collaborative and multidisciplinary environment and gain hands-on experience in applied research at the intersection of AI and behavioral science.
Requirements
Master’s degree (or near completion) in Computer Engineering, Telecommunications, Electrical Engineering, or Computer Science;
Interest in Natural Language Processing and Large Language Models;
Good programming skills (preferably Python);
Familiarity with LLM libraries and frameworks (e. g. , OpenAI API, Hugging Face Transformers);
Good communication and collaboration skills;
Fluency in written and spoken English (minimum B2 level; no certificate required).
Internship Information
Type: Curricular internship (no allowance);
Duration: 3 to 6 months, depending on the candidate’s needs and preparation;
Location: Remote, hybrid or in presence at Povo ( Trento, Italy).
Other benefits:
Access to the internal canteen (except for UniTN students);
Supportive, interdisciplinary research environment;
Support for the search for accommodation at the affiliated structures (no allowance).
Additional Opportunities:
This internship may be extended into a full research thesis for interested candidates.

How to Apply
Interested candidates should submit their application by filling out the online form available in the “ Internship Opportunities” section at , attaching the following documents in PDF format:
Curriculum vitae
Motivation letter
For any information, contact Simone Bassanelli (sbassanelli@fbk. eu)