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PhD position Cross-Sensor Transfer Learning for Long-Term Forest Biomass and Structure Estimation Using Established and Next-Generation SAR Missions(m/w/x)
Developing cross-sensor transfer learning frameworks for forest biomass estimation using SAR data. Strong background in Earth observation and SAR processing required. Flexitime, work from home, and childcare services.
Requirements
- Above-average M.Sc. degree or equivalent
- Strong background in Earth observation
- Strong background in remote sensing of forests
- Strong background in SAR data processing
- Interest in Earth observation
- Interest in remote sensing of forests
- Interest in SAR data processing
- Experience with machine learning methods
- Experience with domain adaptation methods
- Experience with transfer learning methods
- Excellent communication skills
- Enthusiasm for international research environment
- Enthusiasm for multidisciplinary research environment
- Willingness to undertake research placement
- Compliance with formalities at both institutions
Tasks
- Develop cross-sensor transfer learning frameworks
- Estimate above-ground biomass using polarimetric SAR features
- Enrich biomass estimates with Polarimetric InSAR and Tomographic SAR
- Explore multi-modal and multi-model support with optical and hyperspectral data
- Characterize sensor-aware uncertainty and error transfer
- Decompose error budget and estimate precision loss
- Validate models across long-term forest observatories
- Ensure transferability across forest types and climatic zones
Education
- Master's degree
Languages
- English – Business Fluent
Tools & Technologies
- SAR data processing
- machine learning
- domain adaptation
- transfer learning
Benefits
Flexible Working
- Flexitime
- Work from home
Healthcare & Fitness
- Sports and leisure offers
Childcare
- Child care services
- Holiday care services
Public Transport Subsidies
- Job Ticket Baden-Württemberg
Free or Subsidized Food
- Canteen food
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PhD position Cross-Sensor Transfer Learning for Long-Term Forest Biomass and Structure Estimation Using Established and Next-Generation SAR Missions(m/w/x)
Developing cross-sensor transfer learning frameworks for forest biomass estimation using SAR data. Strong background in Earth observation and SAR processing required. Flexitime, work from home, and childcare services.
Requirements
- Above-average M.Sc. degree or equivalent
- Strong background in Earth observation
- Strong background in remote sensing of forests
- Strong background in SAR data processing
- Interest in Earth observation
- Interest in remote sensing of forests
- Interest in SAR data processing
- Experience with machine learning methods
- Experience with domain adaptation methods
- Experience with transfer learning methods
- Excellent communication skills
- Enthusiasm for international research environment
- Enthusiasm for multidisciplinary research environment
- Willingness to undertake research placement
- Compliance with formalities at both institutions
Tasks
- Develop cross-sensor transfer learning frameworks
- Estimate above-ground biomass using polarimetric SAR features
- Enrich biomass estimates with Polarimetric InSAR and Tomographic SAR
- Explore multi-modal and multi-model support with optical and hyperspectral data
- Characterize sensor-aware uncertainty and error transfer
- Decompose error budget and estimate precision loss
- Validate models across long-term forest observatories
- Ensure transferability across forest types and climatic zones
Education
- Master's degree
Languages
- English – Business Fluent
Tools & Technologies
- SAR data processing
- machine learning
- domain adaptation
- transfer learning
Benefits
Flexible Working
- Flexitime
- Work from home
Healthcare & Fitness
- Sports and leisure offers
Childcare
- Child care services
- Holiday care services
Public Transport Subsidies
- Job Ticket Baden-Württemberg
Free or Subsidized Food
- Canteen food
Like this job?
BetaYour Career Agent finds similar jobs for you every day.
About the Company
Institut für Photogrammetrie und Fernerkundung (IPF)
Industry
Science
Description
The Institute of Photogrammetry and Remote Sensing (KIT-IPF) focuses on remote sensing, Earth observation, and applying machine learning to forest dynamics.
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