Automated Temporal Planning (also known as Planning & Scheduling) is the problem of synthesizing a course of actions to achieve a desired goal in scenarios when time, resources and temporal constraints matter. This PhD position focuses on bridging the gap between temporal planning and scheduling, by incorporating global constraints and other scheduling notions into the planning problem representation and researching efficient solution approaches. Planning algorithms are powerful for selecting the actions needed to reach the goal, but often struggle with resource capacity constraints and temporal intricacies inherent in real-world applications. This research aims to develop novel, hybridized models that seamlessly merge symbolic planning representations (e.g., PDDL) with the expressive power of global constraints commonly found in Constraint Programming (CP) and scheduling domains (e.g., cumulative, all-different, resource envelope constraints). The core objective is to design and evaluate new representation languages, formal semantics, and corresponding solution techniques, including heuristic search, constraint propagation mechanisms, and specialized relaxation methods, that leverage the combined strengths of both fields. The resulting framework is expected to yield significant improvements in scalability and the ability to solve complex, tightly constrained planning and scheduling problems across various domains, such as logistics, manufacturing, and spacecraft operations. Moreover, efficient solution techniques will be developed by combining the strengths of modern schedulers and automated planners.