Neural Architecture Search (NAS) is a subfield of machine learning that focuses on automating the design of neural network architectures. Traditional approaches to building neural networks often rely on human expertise and trial-and-error, but NAS aims to discover optimal architectures automatically. By doing so, NAS can lead to state-of-the-art models in various domains, including computer vision, natural language processing, and reinforcement learning.Neural Architecture Search (NAS) has the potential to automate the design of high-performance neural networks across a wide range of tasks and domains. The above PhD project ideas cover a variety of topics, including edge computing, multi-modal learning, adversarial robustness, and self-supervised learning. These projects aim to push the boundaries of NAS research by focusing on efficiency, interpretability, robustness, and adaptability to real-world challenges.