EDUCATION

• PhD degree in Computer Science/Vision, Remote sensing, image or signal processing, machine learning, applied mathematics, telecommunications engineering or similar disciplines.

EXPERIENCE AND SKILLS

The selected candidate will play a central role in the project. Her/his main mission is to develop a fully automatic and globally scalable deep learning model capable of detecting changes affecting infrastructure using remote sensing, multi-modal, and multi-resolution data. Change detection on multimodal remote sensing images has become an increasingly intriguing and challenging topic in the remote sensing community. The technology plays an essential role in time-sensitive applications, such as disaster response, because it can substantially reduce the time to access the information. Various satellite data sets will serve as input (e.g., Sentinel-1, Sentinel-2, Planet, Capella, Maxar). You will work with an international and highly interdisciplinary team of scientists and engineers with expertise in remote sensing (optical and radar), deep-learning and image classification. The candidate will test the algorithm in a real case scenario in collaboration with project partners.
Required Seniority: 2 years of Post-Doc

TECHNICAL SKILLS:

• Advanced knowledge of different Deep Learning and Machine Learning algorithms for supervised, unsupervised, and semi-supervised learning.
• Experience in using and analyzing Earth Observation data (e.g. optical, SAR)
• Proven previous experience in developing workflows in HPC environment.
• Good knowledge of EO toolkits (e.g., GDAL, SNAP, EnMAP box, etc.).
• Excellent programming skills (e.g., Python, C/C++, Matlab, IDL, etc.).
• Experience in applying Deep Learning and Machine Learning algorithms to different data sets and in particular Earth Observation data for classification, image segmentation and geophysical parameters retrieval (e.g., Sentinel-1 and -2, Worldview, TerraSAR-X, COSMO-SkyMed, etc.).
• Hands-on experience with at least one of the following popular Machine Learning/Deep Learning frameworks: Scikit-learn, Tensorflow, Pytorch, and Keras.
• Experience with image processing software.
• Excellent communication skills in presenting scientific research, and writing papers in scientific journal and technical reports.
• Communicative and willing to learn, self-organized, and creative.
• Ability to work both independently and collaboratively in an international team.

Scientific work tasks:

• Developing and coding innovative scientific Deep Learning/Machine Learning algorithms to detect damaged infrastructures caused by natural disasters using SAR and optical data.
• Processing and analysing large collections of optical and radar satellite data.
• Integrating and implementing scientific algorithms on high performance and distributed computing infrastructures to support the development of operational Earth Observation applications, and end-to-end decision support tools.
• Contributing to the development of partnerships and networks at national and international levels.

Project management tasks:

• Establish a continuous communication and effective collaboration with the partners of the project.
• Assist in the preparation of project reports and presentations in project meetings.
• Participate actively in the maintenance of a project-dedicated version-control system (e.g., GitLab).
• Explore and employ cutting edge software packages facilitating the interoperability and reusability of the data generated in the project.

Dissemination, valorisation and transfer tasks:

• Contribute to dissemination, valorisation and transfer of project results (e.g., participation in scientific conferences, exhibition of technology, training sessions, drafting of technical reports, and publication in reputed peer-reviewed scientific journals).
• Participation in the implementation of technological solutions (proof-of-concepts, prototypes).

LANGUAGE SKILLS

• Good level both written and spoken English

Scientific work tasks:

• Developing and coding innovative scientific Deep Learning/Machine Learning algorithms to detect damaged infrastructures caused by natural disasters using SAR and optical data.
• Processing and analysing large collections of optical and radar satellite data.
• Integrating and implementing scientific algorithms on high performance and distributed computing infrastructures to support the development of operational Earth Observation applications, and end-to-end decision support tools.
• Contributing to the development of partnerships and networks at national and international levels

Project management tasks:

• Establish a continuous communication and effective collaboration with the partners of the project.
• Assist in the preparation of project reports and presentations in project meetings.
• Participate actively in the maintenance of a project-dedicated version-control system (e.g., GitLab).
• Explore and employ cutting edge software packages facilitating the interoperability and reusability of the data generated in the project

Dissemination, valorisation and transfer tasks:

• Contribute to dissemination, valorisation and transfer of project results (e.g., participation in scientific conferences, exhibition of technology, training sessions, drafting of technical reports, and publication in reputed peer-reviewed scientific journals).
• Participation in the implementation of technological solutions (proof-of-concepts, prototypes)