Postdoc in Sensing Data Analysis: Join the Department of Electrical and Computer Engineering at Aarhus University for a one-year postdoctoral position focused on deep learning for remote sensing data analysis, aimed at improving sustainable water management through AI-driven solutions.
Postdoc in Deep Learning Based Remote Sensing Data Analysis
Designation
Postdoctoral Researcher in Deep Learning Based Remote Sensing Data Analysis
| Detail | Information |
|---|---|
| Research Area | Deep Learning, Remote Sensing, Groundwater Management |
| Location | Aarhus University, Finlandsgade 22, 8200 Aarhus N, Denmark |
| Duration of Employment | Initial 1-year term (15 Apr 2026 – 14 Apr 2027), with a potential 1-year extension |
| Expected Start Date | April 15, 2026, or as soon as possible thereafter |
Eligibility/Qualification
- Educational Background: PhD in Electrical Engineering, Computer Engineering, Geoinformatics, Computer Science, or related fields with a focus on deep learning applied to remote sensing or geospatial data.
- Experience:
- Documented experience in deep learning model development (e.g., CNNs, UNets, Transformers).
- Experience with satellite data, particularly SAR and multi-spectral imagery.
- Proficiency in Python and familiarity with deep learning frameworks (e.g., PyTorch).
- Strong understanding of geospatial data handling, time series analysis, and model evaluation metrics.
- Publication Record: Strong track record relative to career stage.
- Communication Skills: Excellent written and spoken English.
Preferred Qualifications:
- Experience with deep learning analysis of satellite soil moisture missions (e.g., SMAP, SMOS).
- Knowledge of super-resolution techniques and explainable AI methods (e.g., Shapley values).
- Experience in collaborative, cross-cultural research environments.
Job Description
The postdoctoral researcher will develop, evaluate, and interpret deep learning models for multi-sensor satellite data, focusing on challenges such as spatiotemporal learning and super-resolution. Key responsibilities include:
- Developing models for spatiotemporal fusion of satellite data (e.g., SAR and SMAP) targeting soil moisture.
- Designing and evaluating architectures for remote sensing super-resolution.
- Applying explainable AI methods to interpret model behavior and predictions.
- Publishing research findings and contributing to outreach activities.
- Collaborating with geophysicists and supervising students.
How to Apply
Interested candidates should submit their application via Aarhus University’s recruitment system. The application must include:
- Curriculum vitae
- Degree certificate
- List of publications
- Statement of future research plans
- Teaching portfolio (if applicable)
For reference letters, please state the referee’s contact information during your application.
Last Date to Apply
February 9, 2026, at 23:59 CET
For more information, please contact:
Assistant Professor Muhammad Rizwan Asif
Phone: +4560909831
Email: rizwanasif@ece.au.dk
For application-related inquiries:
Nat-Tech HR
Phone: +4520675285
Email: nat-tech.HR.team2@au.dk
Aarhus University is committed to diversity and encourages applications from all qualified candidates, especially women and individuals from underrepresented backgrounds in STEM fields.
