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PhD or postdoc position on Developing Utility Infrastructure Ontologies and KGs

 We’re looking to hire a student (with full funding, including tuition) or postdoc to help develop a new powerful knowledge network of flooding information – the Urban Flooding Open Knowledge Network (UF-OKN) – that will become the backend for providing accurate flood information to anyone, anywhere, anytime. An overview of the project can be found here:

At the core, this requires integrating knowledge about the location, status, connectivity and dependence between all the different components of urban infrastructures. You will be tasked to help develop and  adapt ontologies about urban utilities (water, sewer, electric, etc.) and connect them to other spatial information relevant to flood prediction, mitigation and disaster response. 

You will work closely with Dr. Torsten Hahmann (Spatial Knowledge and AI Lab, School of Computing and Information Science at the University of Maine) and many other members from the interdisciplinary UF-OKN team spread all across the US. 


1.      MS or PhD degree in Computer Science, Cognitive Science, Geography, Civil Engineering, Industrial Engineering, Mathematics, Physics or a closely related discipline;

2.      Demonstrable experience in at least one of the following areas:

-        Ontologies, controlled vocabularies or taxonomies,

-        Other semantic technologies, such as RDF-S, JSON, Knowledge Graphs, Triple Stores

-        Graph Databases

-        Conceptual Modeling (ER) 

-        Logic (Predicate/First-order Logic or Description Logic)

-        Spatial mapping/GIS involving urban an especially utility infrastructure;

-        Asset or operational management of some kind of utility infrastructure;

3.      If you have no prior degree in Computer Science or a related discipline, evidence of relevant prior experience with programming, databases, knowledge or data management;

4.      Knowledge of emergency response and management would be considered a plus.

For questions or to apply, contact Dr. Torsten Hahmann: Please include a letter described your interest and fit with the project, a current CV, and a sample of your prior work (writing sample such as a paper, project report or thesis, or a description or link to a prior project you have worked on).