Opportunity for Visiting Researchers in Earth Observation for Health Intelligence

Deadline: August 31, 2026 at 23:59 CET

The ESA Φ-lab CIN is now opening applications for visionary researchers and innovators in Transformative Technologies to strengthen ESA Φ-lab’s ongoing and future projects in Earth Observation (EO). Find out more and apply now.

Main Information

🖊️ Topic: AI-powered Earth Observation solutions for health applications

🎓 Open to: Master's students, PhD students, postdoctoral researchers, research engineers, and early-career researchers from academia or industry

📍 Location: ESA Φ-lab, ESRIN, Frascati, Italy

📅 Duration and timing: October or November, can last for 3-6 months

Background

Earth Observation (EO) has become an increasingly important resource for understanding the environmental and climatic drivers of human health. The availability of high-resolution satellite observations, climate reanalysis products, geospatial datasets, and advanced Artificial Intelligence (AI) methods creates new opportunities to develop innovative approaches for monitoring, predicting, and responding to health-related challenges.

Many infectious diseases, including vector-borne diseases such as dengue, malaria, chikungunya, and West Nile virus, are strongly influenced by environmental conditions, including temperature, precipitation, vegetation dynamics, land-use changes, and human mobility patterns. By combining EO data with epidemiological information and AI models, it is possible to develop early warning systems, identify emerging risks, and support public health decision-making.

Recent advances in foundation models, multimodal AI, spatio-temporal modelling, and explainable AI offer new opportunities to move beyond traditional disease modelling approaches towards scalable, adaptive, and globally applicable health intelligence systems.

The ESA Φ-lab Collaborative Innovation Network (CIN) invites highly motivated visiting researchers to contribute to the development of AI-powered Earth Observation solutions for health applications, bridging satellite data, environmental intelligence, and public health challenges.

Research Challenges

Applicants are invited to contribute to one or more of the following research directions:

Primary Research Objectives

OBJ1 – Earth Observation-Based Health Intelligence

Develop innovative AI methodologies for exploiting EO data to monitor, understand, and predict health-related risks, with a particular focus on climate-sensitive diseases and environmental determinants of health.

OBJ2 – Multimodal AI for Disease Monitoring and Prediction

Investigate approaches for combining heterogeneous data sources, including satellite observations, climate data, environmental variables, epidemiological records, and socio-demographic information, to improve health risk assessment and forecasting.

Secondary Research Objectives

OBJ3 – Early Warning Systems and Operational AI

Develop scalable AI-driven approaches for supporting early warning systems by integrating real-time EO observations, climate forecasts, and predictive modelling frameworks.

OBJ4 – Explainable and Trustworthy AI for Health Applications

Investigate methods to improve the interpretability, reliability, uncertainty estimation, and operational usability of AI models for health-related decision support.

Potential Application Areas

Research activities may include, but are not limited to:

• Dengue and other mosquito-borne disease monitoring and prediction;
• Malaria risk assessment and environmental suitability modelling;
• Climate-sensitive disease early warning systems;
• Vegetation, water, and land-surface monitoring for health applications;
• Extreme weather and climate-related health impacts;
• Environmental drivers of infectious disease emergence;
• Integration of EO data into public health surveillance frameworks.

Who Are We Looking For?

We are looking for highly motivated Master's students, PhD students, postdoctoral researchers, research engineers, and early-career researchers from academia or industry who are interested in applying Artificial Intelligence and Earth Observation technologies to health-related challenges.

Selected candidates will join the ESA Φ-lab Collaborative Innovation Network for an onsite research visit, typically lasting 3–6 months, working closely with ESA researchers and collaborators on innovative AI-based approaches for Earth Observation and health applications.

We particularly encourage applicants interested in establishing long-term collaborations through internships, thesis projects, joint publications, or future research opportunities.

Previous experience in both Earth Observation and health applications is appreciated but not required. We encourage applications from researchers with backgrounds in Artificial Intelligence, remote sensing, climate science, epidemiology, data science, and related disciplines.

If selected, ESA will provide an invitation letter (see template here).

Desired Background

Applicants should have experience or strong interest in one or more of the following areas:

• Artificial Intelligence and Machine Learning
• Earth Observation and Remote Sensing
• Geospatial AI
• Climate and environmental modelling
• Public health and epidemiology
• Spatio-temporal modelling
• Deep learning for satellite data
• Time-series analysis
• Explainable and trustworthy AI
• Data fusion and large-scale geospatial analytics

Experience with one or more of the following is desirable:

• Satellite data processing frameworks (e.g., Sentinel, MODIS, Landsat, Meteosat)
• Deep learning frameworks such as PyTorch or TensorFlow
• Large-scale data processing platforms
• AI foundation models and transfer learning
• Climate and environmental datasets
• Health and epidemiological datasets

Strong programming skills in Python and experience with scientific computing workflows are highly desirable.

What We Offer

Visiting researchers will have the opportunity to:

• Collaborate with ESA Φ-lab researchers on cutting-edge AI and Earth Observation research.
• Develop innovative solutions addressing global health challenges.
• Work with satellite observations, climate datasets, AI models, and geospatial analytics frameworks.
• Contribute to open-source software, scientific publications, and international collaborations.
• Explore the integration of AI and EO technologies into future health monitoring and decision-support systems.
• Join a multidisciplinary research environment connecting Artificial Intelligence, Earth Observation, climate science, and public health.

Duration and Location

The visiting research period is typically 3–6 months, although alternative durations may be considered depending on the proposed collaboration.

The research will be conducted onsite at ESA ESRIN (Frascati, Italy) within the ESA Φ-lab Collaborative Innovation Network.

How to Apply

Interested candidates are invited to submit:

• A detailed Curriculum Vitae (CV);
• A motivation letter (maximum one page) describing:
• their background and research interests;
• their motivation for joining the programme;
• how they envision contributing to Earth Observation-based health intelligence;
• Optional supporting material, such as publications, GitHub repositories, project portfolios, or recommendation letters.

Selection Criteria

Applications will be evaluated based on:

• Academic background and technical expertise;
• Experience with AI, Earth Observation, health, or related scientific domains;
• Research potential and motivation;
• Programming and data analysis skills;
• Ability to work in interdisciplinary teams;
• Potential contribution to ESA Φ-lab research activities.

About the ESA Φ-lab Collaborative Innovation Network

The ESA Φ-lab Collaborative Innovation Network (CIN) brings together researchers from academia, industry, and the European Space Agency to accelerate disruptive innovation in Artificial Intelligence and Earth Observation.

The network promotes interdisciplinary research and open innovation across emerging fields including AI foundation models, geospatial intelligence, climate applications, environmental monitoring, and AI-driven solutions for global challenges.

Visiting researchers will contribute to research at the intersection of satellite data, Artificial Intelligence, and societal applications, helping develop new approaches for understanding and addressing environmental and health risks.

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