Domain Adaptation is a subfield of transfer learning that focuses on training models to perform well in a target domain where labeled data is limited or scarce, by leveraging knowledge from a related source domain where labeled data is more abundant. Domain adaptation is critical in real-world applications where data distributions often shift between training and deployment environments.Domain Adaptation is a critical area of research for ensuring that machine learning models generalize well across different data distributions and environments. The PhD project ideas above span various domains, including computer vision, NLP, reinforcement learning, time series forecasting, and privacy-preserving methods. These projects offer opportunities to develop novel domain adaptation techniques that address real-world challenges, improving the robustness and transferability of machine learning models.