AI-Driven Approaches to Understanding Loneliness Across the Life Course and Communities

This PhD project within Sheffield Hallam University's Centre for Collaboration in Community Connectedness (C4) uses advanced quantitative methods and artificial intelligence to investigate loneliness across the life course and diverse communities. Loneliness, a widespread and stigmatized experience in the UK, is complex and challenging to measure. Through machine learning analysis of multi-faceted survey and text data, this research aims to uncover the underlying drivers of loneliness and patterns of self-disclosure. You will apply rigorous machine learning techniques to analyze large datasets, integrating interdisciplinary approaches from social sciences and digital inclusion fields. Supervised by a C4 co-investigator and supported by a broader academic and policy team, you will engage with community catapults to co-design research priorities and delivery. The work will be situated within a vibrant PhD community and benefit from the ESRC White Rose Doctoral Training Partnership network. The project will generate nuanced insights into loneliness that consider social, economic, and cultural contexts. These findings will inform evidence-based policies and interventions for reducing loneliness and fostering social connectedness across different life stages and populations. Loneliness negatively affects millions, impacting wellbeing and social cohesion. This research tackles its latent nature by using AI to produce actionable knowledge that can aid communities and policymakers in creating social environments that promote inclusion, participation, and resilience.

Applicants must hold a strong relevant undergraduate degree (minimum 2.1) and/or a relevant master's qualification (merit minimum). International students need to demonstrate English proficiency via IELTS (overall 7.0, no less than 6.5 in components) within two years or a UK Master's degree at merit level within two years. Applicants must submit a research proposal (max 1,500 words), a personal statement addressing specific questions, copies of qualifications, transcripts, two academic referees, and passport/visa details.

Submit a complete online application form for the October 2026 intake including a research proposal, personal statement, and supporting documents. Attend the online Q&A webinar on 15 April 2026 for more information. The project contact is Professor Andrea Wigfield (andrea.wigfield@shu.ac.uk).