This paper studies the problem of sensor placement design for efficient dynamic real-time state estimation in electric power networks. Given a (linearized) dynamic physical model of the power system, efficient sensor placement strategies are proposed that minimize the observability index of the system. The observability index plays a key role in determining the minimum window length of filters that guarantee stable estimation error and minimizing this index allows the design of memory and computationally efficient filtering schemes with performance guarantees. Specifically, given the system dynamics, the paper addresses the following two sensor placement design problems: (1) determining the minimal number and placement of sensors that achieves a certain desired system observability index, and (2) given the number of sensors to be deployed, obtaining the placement achieving minimal system observability index. These problems are addressed in a structural systems framework, i.e., the placement strategies are obtained on the basis of the sparsity pattern (location of zeroes/non-zeroes) of the system coupling matrix, and the design guarantees hold for almost all numerical parametric realizations of the system. Finally, an example is provided which illustrates the analytical findings.