Assistant Professor (fulltime) In geodata integration, interoperability and distributed analysis

To achieve our ambition of generating new insights and providing actionable geoinformation products to diverse stakeholders, we need to make sense of an ever-increasing volume and diversity of geodata. This requires the anticipation and implementation of new opportunities to collect, integrate, process, and analyze geodata from heterogeneous sources and of highly varying quality. Technological advances allow us to shift (part of) the analysis to the edge of the network and use the entire network to for distributed learning and analysis, requiring new approaches and methods to take full advantage of this development. We are recruiting an assistant professor to strengthen and extend our activities in integration, interoperability, and distributed analysis of geodata.

You will work at the intersection of geodata integration, interoperability, and distributed analysis, including wireless sensor networks and spatially aware machine learning on constrained hardware (Internet of Things). Your central theme will be the methods to handle, combine, and optimize the processing of geoinformation in extremely heterogeneous and data-rich environments to develop spatially explicit, explanatory and/or forecasting models for various application domains (e.g., extreme weather events, air pollution, or noise pollution), while maintaining the privacy of the contributors. In this field, you will help shaping our R&D agenda and ambitions, identify relevant project opportunities, connect with expert colleagues, and build teams and proposals.

You will have regular interactions with staff and PhD candidates to build and sustain an overview of expertise available, and support the ambitions of the University, faculty, and the department towards 2030. Together with project officers you will identify opportunities early in the domains of research, education and capacity development.