This paper describes work-in-progress that uses static timing analysis to aid in making dynamic scheduling decisions. For instance, different algorithms with varying levels of accuracy may be selected based on the algorithm's predicted worst-case execution time and the time allotted for the task. We represent the worst-case execution time of a function or a loop as a formula, where the unknown values affecting the execution time are parameterized. This parametric timing analysis produces formulas that can then be quickly evaluated at run-time so dynamic scheduling decisions can be made with little overhead. Benefits of this work include expanding the class of applications that can be used in a real-time system, improving the accuracy of dynamic scheduling decisions, and more effective utilization of system resources. This paper describes how static timing analysis can be used to aid in making dynamic scheduling decisions. The WCET of a function or a loop is represented as a formula, where the values affecting the execution time are parameterized. Such formulas can then be quickly evaluated at run-time so dynamic scheduling decisions can be made when scheduling a task or choosing algorithms within a task. Benefits of this parametric timing analysis include expanding the class of applications that can be used in a real-time system, improving the accuracy of dynamic scheduling decisions, and more effective utilization of system resources.