A postdoctoral position in areas related to coding and information theory is available at the University of Maryland. The postdoctoral position is related to one of the following broadly defined research directions:
(1) While random codes in a metric space are on average uniformly distributed, deterministic codes may be good or poor approximations to the uniform distribution. There are several ways to measure the quality of the approximation, including discrepancy of the code as well as other metrics of proximity to uniformity. The project seeks to characterize discrepancy minimizers as well as to explore multiple potential applications of uniformly distributed subsets to decoding, derandomization, image processing, and learning. The research methods lie at the intersection of distance geometry, probability, coding theory, and combinatorics;
(2) Codes on graphs form one of the established research directions in coding theory. This project is concerned with storage code on graphs and their extension to recoverable systems. A storage code models distributed placement of information on the vertices of a graph in such a way that the chunk of data on any given vertex can be reconstructed from the values of its immediate neighbors. Constructions of storage codes rely on graph-theoretic methods that also turn up in the construction of quantum codes. The recent extension of graph codes to recoverable systems exhibits unexpected links to constrained coding and methods of entropy theory (entropy-maximizing measures on infinite graphs). This line of research may rely on tools from symbolic dynamics, percolation theory, statistical physics, information and coding theory.