National GIS Specialist – with geomorphology / remote sensing tasks (SSA) - Roster Call

Overview

To develop and operationalize a geomorphology-based risk classification module within the MoES GLOF monitoring system, support aerial surveys with remote sensing analysis, produce technical and community-level GLOF hazard maps from hydrodynamic model outputs, and build national capacity in GIS-based GLOF risk assessment.

Key Responsibilities

• Audit of existing MoES GIS layers, lake inventory and risk-scoring algorithm; preparation of geospatial work plan and data-management protocol.
• Development of the geomorphological indicator framework and definition of classification protocols.
• Remote sensing screening of priority lakes to support selection of aerial-survey targets.
• Participation in aerial survey mission planning and onboard observations; post-mission geotagged photo cataloguing, georeferencing and integration into the GIS database.
• Translation of geomorphological indicators into spatial datasets and integration into the multi-criteria risk-scoring algorithm.
• Production of technical and simplified community hazard maps.
• Preparation of the final GIS technical report; participation in the project closure workshop; formal handover of all geospatial datasets, code, web map services and documentation to MoES.

Required Experience

Minimum 5 years of professional experience in GIS analysis and remote sensing applied to mountain hazards (debris flows, GLOFs, landslides); Demonstrated experience with the geomorphological characterization of moraine-dammed lakes and dam-stability indicators; Experience producing cartographic products for emergency-management and community-level audiences; Familiarity with OSCE or comparable international-organization procedures is an asset; Previous experience of conducting research, data collection and analysis would be an asset.

Qualifications

Advanced university degree (Master’s or PhD) in geography, geoinformatics, engineering geology, geomorphology or a closely related discipline; Proficiency in QGIS, ArcGIS, SNAP, Google Earth Engine and Python or R for geospatial analysis; Working knowledge of optical and SAR satellite data (Sentinel-1/2, Landsat, ALOS PALSAR) and DEM products; Computer literate with practical experience using Microsoft applications.

Other Details