New affordable in-situ sensors are part of the Internet of Things (IoT) and enable us to build denser environmental sensor networks that measure phenomena more efficiently which vary greatly in space and time, such as air or noise pollution. However, such dense wireless sensor networks can put additional strain on bandwidth in areas with less network coverage, and they threaten citizens' privacy. One solution is to move the processing and analysis of collected geodata to the sensors. This type of edge computing has been made possible by advances in deep learning methods on embedded hardware, e.g., Tensorflow Lite for microcontrollers.
You will support our ambitions by working together with colleagues from several departments on projects that use small, affordable sensors (embedded devices) to collect environmental data and submit this data wirelessly. This position will therefore advance our understanding of processing geodata on the (network) edge and combine different geodata sources (remote and in-situ) in reproducible, distributed (networked) workflows. More specifically, you will advance the state-of-the-art in embedded devices and Internet of Things technologies (hardware platforms, network protocols) by designing software solutions for environmental sensing using diverse open hardware platforms, adapting and implementing major developments in the application of machine learning / AI on embedded devices, and propose and implement new workflows that incorporate IoT technology in research and capacity development projects.
Information and application
For more information, you can contact Frank Ostermann (e-mail: firstname.lastname@example.org) or Karin Pfeffer (email@example.com). You are also invited to visit our homepage.
Please submit your application before 7 October 2023 . Your application has to include (i) a letter outlining your motivation and fit for the position, and (ii) a CV. Job interviews will be held in week 42 (1st round) and week 44 (2nd round).
About the department
The Department of Geo-information Processing (GIP) is a multi-disciplinary scientific department that develops computational methods for processing spatiotemporal data, which in turn are used to build models, visualizations and information systems to improve our understanding of dynamic spatial systems and to help in decision-making at multiple spatial and temporal scales. GIP is not tied to a single application domain but develops generic geo-information and geo-products that are widely applicable.
About the organisation
The Faculty of Geo-Information Science and Earth Observation (ITC) provides international postgraduate education, research and project services in the field of geo-information science and earth observation. Our mission is capacity development, where we apply, share and facilitate the effective use of geo-information and earth observation knowledge and tools for tackling global wicked problems. Our purpose is to enable our many partners around the world to track and trace the impact – and the shifting causes and frontiers – of today’s global challenges. Our vision is of a world in which researchers, educators, and students collaborate across disciplinary and geographic divides with governmental and non-governmental organisations, institutes, businesses, and local populations to surmount today’s complex global challenges and to contribute to sustainable, fair, and digital societies.