The LEGO Foundation Fellowship (SSRC)

Administered by the Social Science Research Council (SSRC), this global initiative supports early- and mid-career researchers dedicated to understanding how children thrive. The fellowship focuses on three critical themes: supporting the youngest children in crisis and conflict settings, promoting the inclusion and wellbeing of neurodivergent children (specifically Autism and ADHD), and exploring children’s learning and development in an AI-enabled world.

The fellowship provides substantial, flexible support over a three-year period, allowing researchers to pursue ambitious and practically relevant work. It is designed for scholars whose research has the potential to bridge the gap between academic evidence and real-world impact. Fellows join a global cohort, participating in meaningful exchanges with peers and engaging with policy and practice audiences to ensure their findings reach those who can implement change.

The program is particularly interested in research that addresses the holistic ecosystems shaping childhood—from families and schools to entire societies. Whether exploring mechanisms of resilience in humanitarian emergencies or identifying how AI reshapes adult-child connections, the fellowship seeks to generate evidence that matters for children’s lives. This is a premier opportunity for researchers to lead high-stakes projects with the backing of one of the world’s most recognized foundations for child development.

Target Audience: Early- and mid-career researchers across disciplines such as education, psychology, public health, data science, humanitarian studies, and disability studies.

Geographic Eligibility: Global.

Tangible Benefits: Up to USD 200,000 in flexible research support over three years, membership in a global cohort of scholars, and opportunities for high-level policy and practice engagement.

Application Deadline: July 31, 2026 (Applications open June 1)

Program Duration: 3 years (February 2027 – December 2029)

Location : Global

Categories : Machine Learning . Others

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