Conduct PhD research developing visual computing and machine learning methods for reality capture and digital twinning of existing buildings. The project focuses on using photographs and other data sources, vision-language models, and generative methods to automatically create precise building information models supporting sustainable building reuse and circular construction.

Conduct PhD research on AI-driven optimization of nano-laser-based optical links. Develop reinforcement learning, gradient-based learning, signal pre-distortion, pulse-shaping, and physics-informed neural network methods to improve spectral and power efficiency. Build physical and data-driven laser models, implement fiber-optic transmission systems, conduct experiments in practical testbeds, maintain research code on GitHub, and coordinate experiments with collaborators.

PhD research on reliable, sustainable, and self-adaptive AI for embedded, edge, and distributed systems. Develop models and runtime mechanisms to characterize and control uncertainty, build prototypes on resource-constrained devices, experimentally validate solutions, publish results, and contribute to teaching and collaboration with academic and industry partners.