Position Assistant Professor
Department(s) Mathematics and Computer Science FTE 1,0
Date off 11/12/2020
Reference number V32.4689
Assistant Professor in Stochastic Operations Research | Technische Universiteit Eindhoven
The Statistics, Probability and Operations Research (SPOR) cluster of the Department of Mathematics and Computer Science at the Eindhoven University of Technology (TU/e) invites applications for a full-time tenure-track position in the area of Stochastics Operations Research (SOR). Within the SPOR cluster, SOR is one of the four disciplinary pillars, with one full-time and three part-time full professors, one associate professor, four assistant professors, three post-docs, and around 15 PhD students.
As a newly appointed faculty member, you will be expected to contribute to the SOR research activities at all levels. You will be encouraged to develop research initiatives within SOR and establish links with broader threads in data-driven modelling and optimization. You will further be expected to engage in the teaching activities of the SPOR cluster and to build and strengthen connections within the cluster as well collaborations with other departments at TU/e and external parties. A proven ability to pursue application areas that expand the current research frontiers or forge links with related scientific fields, is considered an important asset, and active involvement in interdisciplinary research projects is encouraged. Experience in acquiring external funding provides a significant advantage.
The research in SOR focuses on evaluating and optimizing the performance and reliability of large-scale systems that operate in the presence of randomness and uncertainty. We develop mathematical models and methods for the design, analysis, optimization and control of such systems with techniques at the intersection of applied probability and operations research. Typical examples of such systems are communication networks, data centers, energy systems, supply chains, transportation networks and hospital operations. Although the specific features of these systems differ, strong commonalities arise from the increasingly heterogeneous characteristics and complex interactions on the one hand and highly stringent performance requirements and huge scalability challenges on the other hand. Furthermore, the advance of digital technology yields abundant historical and real-time data as well as massive computational power for analyzing and improving the performance of these systems.
While inspired by applications, the research approach is foundational in nature and driven by mathematical rigor. Key methodological tools are stochastic processes, random walks, queueing theory and asymptotic scaling methods for higher-dimensional Markov processes.
