The EO Data Science department develops advanced signal processing and AI methods for current and future Earth observation missions. It is involved in a large number of third-party funded projects and a large international network. The tasks to be performed consist of own scientific research on the topic of Artificial Intelligence for Earth Observation as well as project work on the development of distributed machine learning (federated learning) for geodata from optical multispectral cameras and radar sensors using Self-Supervised Learning (SSL) where the fusion of multiple sensor modalities across the boundaries of a single data center plays an important role.
We are looking to hire two new employees with complementary and interacting work focus:
1. Method development and prototype benchmarking
2. MLOps development and method refinement
Specifically, your scope of work will include the following tasks:
• Literature review on self-supervised learning, Kl-assisted data compression, data fusion of remote sensing data (specifically Sentinel-1/2, but also other missions such as EnMAP), distributed machine learning, MLOps development.
• Develop specific benchmarks and software for implementing distributed machine learning on geodatabases beyond the boundaries of individual data centers.
• Scientific supervision of bachelor and master theses within the scope of the above-mentioned field of activity and research
• Preparation of the project report in collaboration with other team members, in particular summary of research results and objectives achieved.
• Participation in publication of research results in appropriate publication organs
Look forward to a fulfilling job with an employer who appreciates your commitment and supports your personal and professional development. Our unique infrastructure offers you a working environment in which you have unparalleled scope to develop your creative ideas and accomplish your professional objectives. Our human resources policy places great value on a healthy family and work-life-balance as well as equal opportunities for persons of all genders (f/m/x).Individuals with disabilities will be given preferential consideration in the event their qualifications are equivalent to those of other candidates.
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