PreDoc Research Associate in Geoinformatics and Remote Sensing

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Position

PreDoc Research Associate in Geoinformatics and Remote Sensing

Employer

University of Salzburg

The University of Salzburg is a thriving university in Austria with a strong focus on research. The university stands for outstanding achievements in both research and teaching in the areas of (digital) humanities, life sciences, sustainability of (social) processes and the link between art and science. With the four guiding themes of Art in Context, Development & Sustainability, Digital Life and Health & Mind, the university offers researchers excellent opportunities for inter- and transdisciplinary cooperation.

The Department of Geoinformatics – Z_GIS is an interdisciplinary centre of competence for Geoinformatics and Earth observation (EO). By applying innovative spatial concepts and methodologies, we contribute to advancing research and to the sustainable management of our societies and environments. The methodological knowledge enriched with a diversified domain expertise provides a fertile ground for the development of new methods and applications thereof.

The Risk, Hazard & Climate Lab at the department aims to identify spatial patterns of risk, hazard and vulnerability through geospatial analysis and remote sensing, integrating inter- and transdisciplinary approaches. To measure, monitor and represent the growing complexity of environmental and societal processes, the lab develops and applies innovative spatial methods and EO data to support a science- and knowledge-based approach to hazard and risk management.

Homepage:

Location

Salzburg, Austria

Sector

Academic

Relevant divisions

Earth and Space Science Informatics (ESSI)

Geomorphology (GM)

Natural Hazards (NH)

Type

Part time

Level

Entry level

Salary

This is a 30 hours/week position. The minimum gross annual salary for a full-time (40h/week) position is € 52,007.20, with the possibility of higher pay depending on qualifications and experience.

Required education

Master

Application deadline

19 November 2025

Posted

7 November 2025

Job Description
You will work on developing methods for automated mapping of geomorphological features and natural hazards using various remote sensing data (optical and SAR satellite data, DEMs, aerial photography), combining object-based image analysis, deep learning, big EO data, and raster and vector data cubes for spatio-temporal processing. You will also have the opportunity to pursue a PhD as part of the project.

Your Responsibilities

• Automated mapping of geomorphological features and natural hazards (e.g. landslides) using various remote sensing data (optical satellite and aerial imagery, synthetic aperture radar (SAR) data, and digital elevation models (DEMs)) using object-based image analysis and deep learning methods;
• Analysis of big Earth observation (EO) data for change detection and time series analysis
• Development and use of raster and vector data cubes for geospatial analysis
• Field validation of mapping results and collection of reference data
• Publishing scientific articles in international peer-reviewed journals
• Presentation of research findings at scientific conferences
• Opportunity to pursue a PhD within the framework of the MorphEO (Big Earth observation for geomorphic mapping and monitoring) project

Desired Qualification

• Excellent knowledge of remote sensing and geospatial data analysis
• Very good programming skills (Python, R, etc.)
• Experience in deep learning methods
• General knowledge of Earth Observation Data Cubes (EODCs)
• General knowledge of geomorphology and natural hazards
• Very good command of written and spoken English
• Desirable personal qualities: ability to work in a team, good communication skills, reliability, creativity, independence, enthusiasm, a positive attitude, flexibility, interest in interdisciplinary research

How to apply

The Full Job Advertisement And The Link To The Application Portal, Where You Can Upload Your Application Documents (CV, Letter Of Motivation, Transcripts, Etc.), Can Be Found Here

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