Recommender systems are pivotal in shaping our online experiences, influencing the content we engage with, the products we purchase, and our social interactions. These systems, driven by machine learning, support a wide range of human decision-making processes across e-commerce, social platforms, entertainment, transportation networks, healthcare services, and cybersecurity.While traditionally evaluated by their accuracy, there's a growing recognition within the research community of the importance of broader evaluation metrics. Fairness, diversity, robustness, explainability, transparency, and privacy are now seen as essential pillars for the overall health and sustainability of these systems. Trustworthy recommender systems are those that not only provide accurate and relevant recommendations but also prioritize these principles to foster user trust and confidence.We Invite Prospective PhD Candidates To Engage In Cutting-edge Research Focused On The Development, Evaluation, And Enhancement Of Trustworthy Recommender Systems. Candidates Will Explore Various Aspects Of Trustworthiness, Including But Not Limited To