PhD Candidate “Deep Learning for Mapping Glacier Areas, Surface Characteristics and Mass Balance”
Your tasksGlaciers are under pressure in the current climate warming trend. The aerial extent and mass balance, a measure of mass loss or mass gain of a …
ongeveer 2 maanden geleden
- Drienerlolaan, Enschede, Overijssel
- Tijdelijk contract / Tijdelijke opdracht
- Uren per week:
- 40 - 40 uur
- € 2395 - € 3061 per maand
Glaciers are under pressure in the current climate warming trend. The aerial extent and mass balance, a measure of mass loss or mass gain of a glacier, represent the "health state" of a glacier. They are recognized as essential climate variables by the world meteorological organization. Due to the sheer number of glaciers of roughly 200.000 worldwide and their inaccessibility, satellite data have been a valuable source to map glacier area and glacier mass balance around the globe during the last decades.
Earth observation satellites, including optical and Synthetic Aperture Radar (SAR) sensors, allow for systematic and continuous glacier monitoring worldwide. Satellite images have enabled researchers to map the extent of glaciers and glacier surface types (glacier facies, i.e., snow, firn, ice). Multi-temporal information on the glacier surface elevation and proxies (snow cover, albedo) which are highly correlated with the glacier mass balance, have been used to estimate mass changes. Today, the ever-growing amount of satellite data motivates the development of new methodologies based on machine learning to optimize the information retrieved from large data sets of multi-modal and multi-temporal satellite data.
The PhD research activity aims to design deep learning methods to automatically analyze temporal series of remotely sensed data and map glacier area, glacier surface characteristics, and infer glacier mass balance. The PhD candidate will take advantage of the latest developments in convolutional neural networks to design deep architectures capable of fusing optical and SAR data for glacier facies analysis within the same network. Combining multiple data sources within a big geodata analysis framework, the candidate will design a deep learning approach to infer the glacier mass change.
- You have an MSc degree in remote sensing, computer science, electrical engineering, geoscience, physics or a related field
- You have a strong background in machine learning, remote sensing, statistics, and strong skills in programming (e.g., in Python, R)
- You have experience with remote sensing data analysis
- You are passionate about addressing environmental problems and curious about monitoring glaciers
- You have demonstrated scientific creativity that has preferably resulted in a scientific publication
- You have excellent communication skills and good English language proficiency
- You have an affinity with a multi-cultural education environment, excellent work ethics, and commitment to the job
We offer a position in an inspiring and challenging multidisciplinary and international environment for a period of four years. You will work in close collaboration with researchers at the University of Oslo (Norway) and the EURAC research institute (Italy). Salary and conditions will be in accordance with the Collective Labor Agreement (CAO-NU) of the Dutch Universities.
- A starting salary of € 2,395.00 in the first year and a salary of € 3,061.00 in the fourth year gross per month;
- A holiday allowance of 8% of the gross annual salary and a year-end bonus of 8.3%;
- A solid pension scheme;
- Minimum of 41 holiday days in case of full-time employment;
- Professional and personal development programs;
- Costs for moving to Enschede may be reimbursed.
Additional informationInformation and application
For more information about this position, you can contact Dr. C. Persello (firstname.lastname@example.org) or Prof. Dr. Ir. A. Stein (email@example.com). You are also invited to visit our homepage.
Please submit your application before 31 August 2021 (choose "apply now" below). Your application must include:
- A cover letter (maximum 2 pages), emphasizing your specific interest in the position and outlining your relevant skills and experience, including a list of names and contact information for three referees
- A full Curriculum Vitae
- Official transcripts from all universities attended