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 student at TU Dortmund University and enrolled in the Data2Action Doctoral Network, with secondment opportunities in German public sector and non-profit organizations.

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 Public sector actors as users and enablers of artificial intelligence to promote social innovation will play a central role in the network and will:

1) conduct a systematic literature review across multiple disciplines, using Python scripts (depending on prior experience) and/or AI tools like Elicit, to create a knowledge base on social innovation in or driven by the public sector, thus delivering a systematized overview of concepts, theories, variables, scales in the field of social innovation in the public sector. Expected result: a critical review and schematisation of existing literature on public sector social innovation in general as knowledge base for scholarship

2) develop a conceptual model incorporating actor, practice, and technology perspectives, deploying a mixed-methods approach, including surveys based on established scales (such as digital orientation) and qualitative techniques like fuzzy set analysis, diary studies, or interviews, with participants recruited from various municipalities (e.g. City of Schwerte) – to explain in how far public sector actors can build upon or provide AI to foster social innovation. Expected result: new measurement instruments and framework on social innovation in the public sector, arranged in a cross-disciplinary approach to create novel empirical data on social innovation in the public sector in connection with the use and provision of AI

3) subsume implications for public sector actors, that is to illustrate opportunities and challenges of using and providing AI to promote social innovation as guidance for public sector organisations and their managing employees. Expected result: a new guideline of opportunities and challenges of using and providing AI to promote social innovation that help public sector actors better foster social innovation

Apart from that, the fellow will undertake two secondments at 1) the Smart City Office of the City of Schwerte to gain experience in a medium-sized public sector organisation on smart city solutions to address societal challenges, and 2) Business Metropole Ruhr, the economic development agency for the whole Ruhr Area, to experience how local public authorities tackle grand challenges from a multisectoral perspective.

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.