Multiple Instance Learning (MIL) is a type of supervised learning where labels are assigned to sets or "bags" of instances, rather than individual instances. In MIL, the goal is to make predictions based on bags of instances, where each bag may contain both relevant and irrelevant instances, and only the bag-level label is known. This framework is highly applicable in fields such as medical image analysis, drug discovery, and document classification, where labeling individual data points may be difficult or impractical.Multiple Instance Learning (MIL) offers a wide range of exciting research opportunities across domains like medical image analysis, object detection, drug discovery, and more. These PhD project ideas span both theoretical advancements in MIL algorithms and practical applications in real-world problems, providing a rich landscape for exploration.