Post Doctoral Researcher in Remote sensing

University of Doha for Science and Technology (UDST) is the first national applied University in the State of Qatar, offering applied bachelor's and master's degrees in addition to certificates and diplomas in various fields. UDST has over 50 programs in the fields of Engineering Technology and Industrial Trades, Business Management, Computing and Information Technology, Health Sciences, Continuing and Professional Education and more. With more than 700 staff and over 8,000 students, UDST is the destination for top-notch applied and experiential learning. The University is recognized for its student-centered learning and state-of-the-art facilities. Our faculty are committed to delivering pedagogically-sound learning experiences with the incorporation of innovative technological interventions, to further enhance students' skills and help develop talented graduates that can effectively contribute to a knowledge-based economy and make Qatar's National Vision 2030 a reality.

The Applied Research, Innovation and Economic Development Directorate invites applications for the position of Post Doctoral Researcher to work on of the UDST Quantum Materials Project.

The Postdoctoral Researcher will contribute to developing an early warning system for dust and air quality prediction in Qatar. The project integrates remote sensing, geospatial data science, and predictive modeling to create real-time forecasting tools supporting public health resilience and climate adaptation. The researcher will work within a multidisciplinary team at UDST and collaborate with national and international stakeholders, including government agencies, industry partners, and academic institutions

• Design and implement a comprehensive data collection strategy integrating natural, built, and societal factors using satellite imagery, ground sensors, and urban datasets.
• Develop predictive models for dust events, particulate matter (PM) concentrations, and air quality dynamics using geospatial regression, Getis-Ord Gi*, and Copula-based statistical techniques.
• Build a cloud-enabled predictive framework (Google Earth Engine, AWS, or Azure) automating data cleaning, processing, and forecasting.
• Conduct hotspot and temporal trend analyses to assess relationships among soil moisture, wind, vegetation cover, and PM concentrations.
• Generate high-resolution environmental maps of dust susceptibility and vegetation-sheltering efficiency.
• Collaborate on extending Quantitative Microbial Risk Assessment (QMRA) models to evaluate health impacts associated with airborne fungal spores.
• Support the design of interactive dashboards or decision-support systems for stakeholder engagement.

• Ph.D. in Electrical Engineering, Environmental Engineering, Geospatial Data Science, Atmospheric Science, Remote Sensing, or a closely related field.
• Demonstrated expertise in remote sensing and predictive environmental modeling.
• Strong programming proficiency in Python (NumPy, Pandas, Scikit-learn, PyMC, GeoPandas) or R for spatiotemporal data analysis and machine learning.
• Proven experience working with satellite datasets (e.g., MODIS, Sentinel, Landsat) and environmental datasets (e.g., soil, vegetation, meteorological, and PM data).
• Experience with GIS platforms (ArcGIS, QGIS) and cloud-based analytics environments (Google Earth Engine, AWS, or Azure).
• Background in statistical modeling, regression analysis, and geospatial methods (e.g., Getis-Ord Gi*, Moran's I, Copula modeling).
• Excellent written and verbal communication skills with a record of peer-reviewed publications.
• Demonstrated ability to work independently and collaboratively in multidisciplinary, multi-institutional research teams.