Technical Assistant for Develop AI/ML Models to Detect and Map Household Locations and Road Networks Using Satellite Imagery សំរាប់ Regional Artemisinin-Resistance Initiative-4 Elimination (RAI4E)
| សំរាប់ : | ជនជាតិខ្មែរ |
| ផ្នែក : | កំព្យូទ័រទូទៅ, កំព្យូទ័រ តបណ្តាញ Network, វិស្វ័កកម្ម |
| ពេលធ្វើការ : | ពេញម៉ោង |
| ភេទ : | ប្រុស, ស្រី |
| ភាសា : | ខ្មែរ, អង់គ្លេស |
| តំបន់ : | ភ្នំពេញ |
| ចំនួន : | 1នាក់ |
| ឈប់ទទួលពាក្យ : | ថ្ងៃទី 02 ខែ តុលា ឆ្នាំ2026 |
| ប្រាក់ខែ : |
តាមការចរចារ
|
| បទពិសោធន៍ : | យ៉ាងហោចណាស់ 7ឆ្នាំ |
| សញ្ញាបត្រ : | អនុបណ្ឌិត |
CNM has been strengthening the use of digital technologies, geospatial information, and data-driven approaches to support malaria surveillance, program management, planning, implementation, and decision-making.
Accurate and up-to-date geographic information on household and building locations, road and footpath networks, and forest cover can support improved mapping, geographic analysis, accessibility assessment, and planning of field activities.
Traditional methods for collecting and updating geospatial data can be time-consuming and resource-intensive. Advances in satellite imagery and Artificial Intelligence/Machine Learning (AI/ML) provide an opportunity to automate the detection and mapping of key geographic features.
CNM therefore intends to develop AI/ML models using satellite imagery from Sentinel, Landsat, and Planet to detect and update household/building locations, road and footpath networks.
To support this initiative, CNM is seeking a Technical Assistants as below:
- Assessment and Planning
- Review available satellite imagery and relevant geospatial datasets.
- Assess the availability and suitability of Sentinel, Landsat, and Planet imagery.
- Identify data requirements and develop a proposed methodology and implementation plan.
- Development of AI/ML Models
- Develop models to identify household and building locations from satellite imagery.
- Develop models to identify and extract road networks.
- Model Testing and Validation
- Test the developed models using selected geographic areas and available reference data.
- Review the accuracy and quality of model outputs.
- Identify limitations and refine the models based on validation results.
- GIS Outputs and Documentation
- Generate GIS-compatible outputs and organize datasets for further analysis and use by CNM.
- Prepare technical documentation describing the models, methodology, data requirements, and outputs.
- Knowledge Transfer
- Provide technical guidance and knowledge transfer to relevant CNM staff.
- Expected Outputs
- Assessment report and implementation methodology for AI/ML model development.
- AI/ML models developed for household/building detection and road.
- Tested and refined models based on validation findings.
- GIS-compatible datasets and technical documentation prepared.
- Technical guidance and knowledge transfer provided to relevant CNM staff.
- Khmer - Good
- English - Good
- Master's Degree in Artificial Intelligence, Machine Learning, Remote Sensing, GIS, Computer Science, Geography, or another related field
- AI/ML, remote sensing, satellite imagery analysis, or geospatial data processing for 7 years as a minimum
- Demonstrated experience in developing AI/ML models for geographic feature extraction, image classification, object detection, or related applications.
- Experience working with Sentinel, Landsat, and/or Planet satellite imagery.
- Strong knowledge of GIS and geospatial data processing.
- Experience in preparing technical documentation and providing technical guidance or capacity building.
- Familiarity with the health system in Cambodia is desired.
- Experience in malaria or public health is desired.
នៅពេលអ្នកដាក់ពាក្យសំរាប់ការងារនេះ ប្រសិនបើអាចសុំជួយប្រាប់ទៅកាន់ក្រុមហ៊ុនថា "អ្នកបានឃើញការងារនេះក្នុងគេហទំព័រ www.khmeronlinejobs.com".
អរគុណទុកជាមុន,
ពីក្រុមការងារ ខ្មែរអនឡាញចប
| ឈ្មោះក្រុមហ៊ុន : | Regional Artemisinin-Resistance Initiative-4 Elimination (RAI4E) |
| អុីម៉េល : | cnm.recruitment@gmail.com |
| អាស័យដ្ឋាន : | No. 477, Concrete Road, Corner of Road 92, Trapeang Svay, Sangkat Kouk Khleang, Khan Sen Sok, Phnom Penh, Cambodia |
