This fully funded PhD project at The University of Manchester, in collaboration with Honda Research Institute (HRI) Europe, aims to develop an adaptive optimization framework for complex socio-technical systems, with a particular focus on energy distribution networks for electric vehicles (EVs). These systems present large, high-dimensional search spaces and require balancing multiple, often conflicting and expensive-to-evaluate objectives such as fairness, explainability, user satisfaction, and environmental impact. Current industry approaches are typically rule-based, slow, and costly, motivating the need for advanced optimization techniques. Building on prior research in expensive, multi-objective, and high-dimensional optimization, the project will explore guided local and global search strategies, intelligent variable subset selection, and the integration of socially derived criteria into optimization objectives. The research will leverage Bayesian Optimization and Gaussian Processes to efficiently navigate expensive search landscapes, exploiting correlations and problem properties to optimize under limited evaluation budgets. The successful candidate will have opportunities to visit HRI Europe and join their global PhD cohort, gaining exposure to industry-driven research and innovation. Applicants should have a strong background in optimization, ideally with experience in multi-objective and expensive optimization, and familiarity with Gaussian Processes. The program is part of the AI UKRI CDT, offering full funding including home tuition fees and a tax-free stipend at the UKRI rate (£20,780 for 2025/26). The start date is September 2026. The University of Manchester is committed to equality, diversity, and inclusion, encouraging applicants from all backgrounds and offering flexible study options. Application requires submission of transcripts, CV, supporting statement, and referee contact details via the university portal. For more information, visit the project and company webpages.