PhD Scholar in Robotics Engineering and Machine Learning at Sheffield Hallam University, where I specialise in digital twins, surrogate modelling, and physics-informed machine learning (PINNs) for real-time simulation and decision support in complex systems. My research integrates computational fluid dynamics (CFD), numerical PDEs, and scientific machine learning. I develop interpretable surrogate models using PINNs, Gaussian Processes, and LSTMs to accelerate simulations in industrial applications. I have worked with top UK academic and industry partners through collaborative workshops and study groups, including Newton Gateway to Mathematics (University of Cambridge), ESGI 187 & 191 (University of Bristol & Leeds), and BIOREME (University of Glasgow). My work has been presented at IEEE IEACon 2024, EFFoST 2023/2024, and international scientific forums. Skilled in Python, MATLAB, COMSOL, PyTorch, TensorFlow, and CFD platforms, I'm passionate about solving real-world engineering problems through AI-physics hybrid approaches. 🔍 Currently open to postdoctoral fellowships, R&D engineering roles, and cross-disciplinary opportunities in applied AI, scientific computing, and digital twin technologies.