This PhD project focuses on achieving unbiased measurements of weak gravitational lensing — the tiny distortions in galaxy shapes caused by the matter along the line of sight. This technique is key to studying dark matter, dark energy, and gravity, and lies at the heart of the Euclid space mission launched in 2023. Traditional shape-measurement methods introduce systematic biases in shear estimation. The goal of this PhD is to develop and extend an innovative forward-modelling approach that directly infers the shear by simulating realistic galaxy images using deep-learning architectures. The student will adapt this framework to real Euclid data, accounting for the complexity of the Science Ground Segment (SGS) and implementing GPU-accelerated and high-performance computing solutions to scale to the full sky coverage. The project is timely, coinciding with Euclid’s first public data release in 2026. The expected outcome is a more accurate and robust shear estimation method, enabling the next generation of precision cosmology analyses.