Internal Research Fellow

Internal Research Fellow (PostDoc) in AI and Earth System Science

**Job Requisition ID**:18922**Date Posted**:30 September 2024**Closing Date**:21 October 2024 23:59 CET/CEST**Publication**:External Only**Type of Contract**:Internal Research Fellow**Directorate**:Earth Observation Programmes**Workplace**:Frascati, IT**Grade Band**:F2 - F2

**Location**
ESRIN, Frascati, Italy**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 in the Directorate of Earth Observation Programmes and in particular in the Earth System Science Hub.
You will be part of the ESA Φ-lab. Our mission is to accelerate the future of Earth Observation (EO) by embracing disruptive innovation and serve as a catalyst for disruptive and transformative innovation in the sector.
-We offer:

- a stimulating multinational, interdisciplinary and open work environment;
- access to high-performance computing infrastructure and unparalleled EO and technology expertise;
- a unique opportunity to work on innovative solutions to address global challenges;
- freedom and focus to conduct creative research while making an impact in relation to ESA’s strategy;
- a wide network of relationships and collaboration with top academia, industry and research centres; the opportunity to contribute to the Φ-lab strategy and activities.
-

**Field(s) of activity/research for the traineeship**
This fellowship is a collaboration between the ESA Φ-lab and the Earth System Science Hub to explore the use of space EO products and advanced AI techniques to advance Earth system science and predictive capabilities for the Earth system and climate research.
In the past number of years, advances in Earth Observation (EO) capabilities have resulted in a broad range of new EO products that are opening up new opportunities to better describe and understand the Earth system. In particular, novel observation products and new geophysical variables observed from space are opening the door to the generation of regional and global multivariate data sets and data cubes, including a wide range of different variables, offering a holistic view of complex processes, such as carbon cycles, hydrological cycles, sea level coastal hazards, ice shelves, ocean-atmosphere interactions and climate change impacts on ecosystems, representing a unique opportunity for the advancement of Earth system science and contributing to the establishment of core components of future Digital Twin Earth replicas of the Earth system.
This activity is aimed at advancing the use of AI and ML in the EO domain to unlock the potential offered by novel, heterogenous multivariate data sets to better characterise, simulate and predict the behaviours of key components of the Earth system and its interactions with human activities and ecosystems. In particular, these activities will focus on at least one of the following challenges:

1. AI-based reanalysis and multivariate reconstruction in common spatial/time grids;
2. Prediction of Earth system dynamics, especially anomalies and extremes;
3. Characterising poorly known and complex processes with a focus on extremes (identification of drivers of change), teleconnections and cascade events.
In particular, you will:

- undertake advanced research activities exploring and expanding the use of disruptive and transformative innovation such as AI, ML, quantum computing and edge computing to develop new frameworks and solutions. Research may cover a wide range of innovative topics, from the development of novel methods, algorithms and ML iterative pipelines to high-level products;
- 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 the uptake of new techniques and capture the latest developments in EO and disruptive/transformative innovation;
- publish the research project outcomes in high-impact journals;
- drive collaboration within the Φ-lab community and ESA internal teams to promote the uptake of these new techniques and solutions;
- contribute to the Φ-lab strategy, activities, and outreach on disruptive technologies for EO;
- maintain a continuous dialogue with the scientific community, including major international programmes and initiatives in the fields of AI, ML, quantum computing and edge computing;
- support the Φ-lab’s research network of Young Graduate Trainees, National Trainees, Interns, and Visiting Researchers, as applicable.

**Technical competencies**
Knowledge relevant to the field of research
Research/publication record
Ability to conduct research autonomously
Breadth of exposure coming from past and/or current research/activities
Ability to gather and share relevant information
General interest in space and space research

**Behavioural competencies**
Result Orientation