Postdoc in Earth Observation Big Data Analytics and Deep Learning - Hiring in process/Finished, not possible to apply

The department of Urban Planning and Environment consists of three divisions and is focused specifically on urban and regional studies, geoinformatics and geodesy. The area of expertise includes research on systems, models and tools for understanding, preserving, changing, managing and developing society, the land and the built environment. The ambition is to analyze the prerequisites, conditions and strategies for sustainable social development, both in national and international context.

The Division of Geoinformatics at the Department of Urban Planning and Environment is responsible for both research and education within the broad field of Geospatial Information Technology (GeoIT). The research at the Geoinformatics Division at KTH is focused on methodology development and the applications of GeoIT for sustainable urban/regional planning, environmental change monitoring, transport analysis, and health studies. The Geoinformatics Division is responsible for the GeoIT Profile in the Built Environment Program and for the International Masters of Science Program in Geodesy and Geoinformatics. The Division also offers GeoIT courses to other disciplines such as Urban Planning and Design, and Real Estate Economics.

Job description

This is a full-time one year position where you as a postdoc is expected to participate in on-going projects, conduct high-quality research on Earth Observation big data analytics, develop innovative methods and algorithms for SAR and optical dense time series processing and analysis using deep learning, writing scientific papers, co-supervise PhD students, and participate in teaching of courses in remote sensing, and image processing.

The on-going projects include:

• EO Big Data for Urban Change Detection
• Spaceborne SARTime Series for Near Real-time Wildfire Monitoring

The specific objectives of the projects include:

- Develop novel change detection methods and algorithms using EO big data and deep learning

-   Evaluate the capacity of multitemporal Sentinel-I/-2 SAR and MSI data for urban change monitoring

-   Evaluate spaceborne SAR data for near real-time wildfire monitoring

Qualifications
A doctoral degree or an equivalent foreign degree, obtained within the last three years prior to the application deadline. PhD in Geomatics Engineering, electrical engineering, computer science, or other natural science and engineering disciplines. The following qualifications are required:

• Strong knowledge in SAR and optical image processing, and machine learning/ deep learning.
• Demonstrated programming skills for implementing relevant methods and techniques in Python and/or C++ and experience in Open Source Code development
• Proven experience in cloud processing platform (i.e. GCP and GEE) for exploitation of EO big data
• Ability to design, implement, and test algorithms for processing and classification of SAR and optical images
• Demonstrated ability to write and publish scientific papers.
• Experience in lecturing and/or teaching lab sessions would be a plus.

Great emphasis will be placed on personal competence and suitability.

Trade union representatives

You will find contact information to trade union representatives at KTH:s web page.

Application

Log into KTH's recruitment system in order to apply to this position. You are the main responsible to ensure that your application is complete according to the ad.

Your complete application must be received at KTH no later than the last day of application, midnight CET/CEST (Central European Time/Central European Summer Time).

The application should include the following documents:

• CV including relevant professional experience and knowledge.
• Copies of PhD degree. Translations to English or Swedish if the original documents are not issued in one of these languages.
• Brief explanation of why you want to conduct research, about your academic interests and how they relate to your previous research and future goals; max 2 pages long.
• Representative publications (Max.: 5): Document no more than 15 pages each. For longer documents (e.g. dissertations), attach a summary (abstract) and a web link to the full text.
• Recommendation letters
• Contact information for two reference persons. We reserve the right to contact references only for selected candidates.

Others

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