We have an exciting and rare opportunity to join the prestigious Horizon Europe Doctoral Training Network, Data2Action, as a Doctoral Fellow. The successful candidate will be registered as a doctoral fellow at Glasgow Caledonian University and enrolled in the Data2Action Doctoral Network, with secondment opportunities in leading “Data for good” organisations.

Data2Action will train 13 Doctoral Fellows as the next generation of transdisciplinary social innovation leaders who are capable of ethically and responsibly developing state-of-the-art data science and artificial intelligence (AI) for social good. The network will bring together leading data science and AI researchers and experts with practitioners and experts in social innovation to conduct empirical research and cascading skill-building with the following objectives: 

Objective 1: Establish a research roadmap and framework grounded in empirical knowledge and best practices for data science/AI to power social innovation (Data/AI for Social Good). 

Objective 2: Demonstrate the potential and benefits across sectors of implementing social innovations powered by data science and AI through five demonstrator projects that showcase the impact on critical societal issues: Climate, Social justice, Democracy, and Health and Ageing.

Objective 3: Build capacity, Data/AI tools and methods for social innovators and entrepreneurs through an integrated open cascade training programme. 

Objective 4: Create new career pathways for data science and AI dedicated to social innovation. 

Objective 5: Provide guidance and support for practice about data science and AI for social innovation to stakeholders who contribute to social innovation: policymakers, funders, and governmental organisations and to data science and AI technology development. 

The Doctoral Candidate in the project The role of AI in enhancing social innovation in health and care for ageing societies will play a central role in the network. The primary objective of this research is to investigate social innovation in health and care service delivery for ageing societies, with a particular focus on how AI and data science can enhance co-production processes. The study will examine the differences between urban and rural areas to explore the five key dimensions of co-production - Who, When, What, Why, and Where - in the context of ageing and healthcare services. 

To achieve this, AI technologies will be employed to systematically review the literature on social enterprise, ageing, and healthcare service provision. Additionally, AI will be used as a tool to facilitate stakeholder engagement in co-creation processes, leading to the development of AI-supported social innovations in service delivery. 

This research will be conducted in collaboration with key partners such as Impact Hub Inverness and the Scottish Council for Voluntary Organisations (SCVO), with a focus on community-based service co-creation and the scalability of innovative care models. By integrating AI-driven insights with participatory methodologies, the study aims to contribute to more inclusive, efficient, and sustainable healthcare solutions for ageing populations. 

To achieve this, the fellow will:  

1) Literature Review and Theoretical Framework Development (i.e. conduct a systematic review of literature on social innovation, co-production, social enterprise, ageing, and healthcare service provision; utilise AI-driven text analysis tools to identify research gaps and emerging themes; develop a conceptual framework for understanding AI-facilitated social innovation in health and care services). 

2) Mapping Urban-Rural Differences in Co-Production (i.e. identify and analyse variations in health and care service delivery across urban and rural settings; examine existing models of co-production, applying the 5Ws framework Who, When, What, Why, Where; collect and synthesise data on policy, funding, and community engagement in different geographic contexts). 

3) AI and Data Science Application (i.e. explore AI methodologies to process literature and stakeholder input; design and propose AI-facilitated social innovation models for community-based healthcare services; assess the ethical implications and limitations of using AI in social innovation). 

4) Stakeholder Engagement and Co-Creation Processes (i.e. collaborate with Impact Hub Inverness, SCVO, and other key partners to engage with practitioners, policymakers, and community groups; organise co-creation workshops to develop AI-facilitated social innovation models; gather and analyse qualitative and quantitative data from stakeholders to refine proposed solutions). 

5) Model Development (i.e. develop scalability strategies to expand successful initiatives across different contexts). 

6) Dissemination and Impact (i.e. present findings at academic conferences, policy roundtables, and community events; publish research outcomes in peer-reviewed journals, policy briefs, and open-access platforms; develop recommendations for policymakers and practitioners on integrating AI and social innovation in ageing care). 

The Doctoral Candidate will be expected to:

• Report on findings by publishing scientific articles, resulting in a doctoral dissertation and oral dissertation defence
• Present findings at (inter)national meetings/conferences
• Contribute to the wider work of the Data2Action project
• Contribute to educational activities of the department and within the consortium.