Our team and mission

Reporting to the Head of the Explore Office in the ESA Φ-lab, you will work in close cooperation with other staff members of the Directorate of Earth Observation Programmes.
You will be part of the ESA Φ-lab. Our mission is to accelerate the uptake of Earth observation (EO) by embracing innovation and acting as the catalyst for transformative innovation in the sector. Our vision is to become an EO innovation hub, connecting a growing ecosystem of transformative technologies, with artificial intelligence (AI) at its heart. Many challenges with new digital technologies still need to be tackled at scientific, applications and capability level to deliver the maximum value from the EO data provided by satellites for our climate, society and economy. The Φ-lab will bring together early career and senior researchers across multiple disciplines in EO and digital technologies to help develop innovative EO solutions.

You are encouraged to visit the ESA website: https://www.esa.int/

Field(s) of activity/research for the traineeship

This research fellowship will be a research opportunity in collaboration with the European Centre for Medium-Range Weather Forecasts (ECMWF). At the end of the two-year stint at ESA, you may have the opportunity to continue your research at the ECMWF as part of the collaboration if the topic and results are deemed valuable.
This forms part of the long-term collaboration between ESA and ECMWF in the field of EO and Numerical Weather Prediction (NWP) and recently ESA and ECMWF joined forces to explore the use of novel methods combining EO data, AI and numerical modelling. As an example, the joint ESA-ECMWF workshop on Machine Learning for Earth System Observation and Prediction [https://www.ml4esop.esa.int] has highlighted the critical need to integrate ML in the prediction pipeline and explore new architectures for scalable, interpretable and physics-informed ML architectures.

In this context, this fellowship will address the use of innovative EO-based solutions to enhance climate resilience by integrating with ML a variety of data types and sources, such as the Internet of Things (IoT), EO and ground-based measurements.

The following themes related to enhancing climate resilience with EO, AI and modelling are of particular interest for the collaboration:

• Improved forecasting of renewable energy production (such as solar) with physics-informed neural networks;
• Live detection and prediction of extreme events (such as floods) with state-of-the-art iterative AI pipelines fusing EO data and modelling capabilities;
• Use of generative AI techniques together with multivariate EO data sets (e.g. Climate Change Initiative) to develop and visualise the new generation of fused EO products supporting climate adaptation.

Within your application, please select one of the above themes and include a short research proposal (of about two pages) on the topic.

In particular, you will:

• undertake advanced research activities exploring the use of state-of-the-art digital techniques such as AI to develop new frameworks to enhance the climate resilience of our society and economy in partnership with wider ESA and ECMWF teams. Research can cover a wide range of innovative topics from the development of novel methods, algorithms and ML iterative pipelines to high-level data fusion products;
• contribute to the development and curation of open data sets and tools enabling the community to develop their own AI for climate resilience applications and research;
• support the definition and implementation of rapid prototyping activities, research sprints and open challenges of innovative EO solutions addressing upcoming lab activities and wider strategy;
• engage with the innovation ecosystem to promote uptake of new techniques and capture the latest developments in EO and digital technologies such as AI, blockchain and new computing paradigms;
• publish the research project outcomes in high-impact journals;
• drive collaboration with the AI4EO and global change community, ESA and ECMWF internal teams to promote the uptake of these new techniques and solutions.

Technical competencies

Behavioural competencies

Result Orientation
Operational Efficiency
Fostering Cooperation
Relationship Management
Continuous Improvement
Forward Thinking

Education

You should have recently completed, or be close to completing a PhD in a related technical or scientific discipline. Preference will be given to applications submitted by candidates having received their PhD within the past five years. In particular, the following is required for this position:

a PhD in data science, AI, computer science, machine learning, Earth system science or climate with the subject of the thesis being relevant to the description of the tasks outlined above.

Additional requirements

You should also have:

• sound knowledge of themes of interest, such as EO, AI, and climate change adaptation and/or mitigation;
• proven experience in leading research, with international recognition;
• the ability to think outside the box and explore new avenues, with natural curiosity and a passion for new subjects and research areas;
• the ability to work in a multicultural environment as part of a team;
• experience with one or more general-purpose programming languages (e.g. Python) and with general-purpose deep learning frameworks such as Tensorflow or PyTorch;
• the ability to deliver high-quality research and publish in high-level journals.

The working languages of the Agency are English and French. A good knowledge of one of these is required. Knowledge of another Member State language would be an asset.