Point defects and dopants strongly influence the electrical, optical, and transport properties of semiconductor materials. Their controlled incorporation is essential for advancing semiconductor technologies, ranging from efficient power electronics to quantum technologies. In particular, wide- and ultra-wide-bandgap semiconductors offer exceptional properties for high-power and high-voltage devices, but their technological development is limited by the availability of suitable dopants and by insufficient understanding and control of native defects and unintentional impurities. This project will develop and apply advanced computational approaches for predictive defect physics, combining high-throughput first-principles calculations, high-accuracy electronic structure methods, and emerging machine-learning techniques. A central objective is to systematically explore large defect and dopant configuration spaces across technologically relevant semiconductor materials. Automated workflows will be used to calculate defect formation energies, charge-state transition levels, doping behavior, and electronic properties. These calculations will enable computational screening for promising dopants and identification of detrimental defects that limit material and device performance. The project will provide the large-scale computational resources required for research within the recently awarded Zenith Career Grant “Data-Driven Investigation of Defects and Dopants for Advancing Semiconductor Technologies” (2025–2031, LiU no. 26.15) and the ÅForsk research grant “Data-Driven Screening of Dopants for Efficient Ultra-Wide-Bandgap Power Converters” (2026–2031, 26-294). A particular focus will be defects and dopants in wide- and ultra-wide-bandgap semiconductors for efficient power conversion. High-throughput calculations will be used to establish relationships between host-material properties, defect chemistry, and dopability, with the aim of identifying design principles and promising material–dopant combinations for next-generation power electronics. By integrating automated high-throughput calculations with high-accuracy first-principles methods and data-driven approaches, the project will establish computational tools and datasets for predictive defect engineering. The results will contribute to a fundamental understanding of defects and dopants and accelerate the identification and optimization of semiconductor materials for efficient power conversion and other advanced semiconductor technologies.