Details
Mission and objectives
With its establishment on 7 April 1948, WHO works worldwide to promote health, keep the world safe, and serve the vulnerable. WHO’s goal is to ensure that a billion more people have universal health coverage, to protect a billion more people from health emergencies, and provide a further billion people with better health and well-being.
Context
The WHO European Centre for Preparedness for Humanitarian and Health Emergencies (PHHE) is a geographically dispersed office of WHO/Europe, established to strengthen the capacities of Member States in the European Region to prepare for, respond to, and recover from health emergencies. PHHE provides technical support, fosters collaboration, and promotes innovative and sustainable approaches to enhance resilience and reduce disaster risk.
The Hub works to enhance the availability, quality, and interoperability of spatial data across the Region. It plays a key role in supporting country offices, technical teams, and response partners by providing timely geospatial analysis and developing shared regional datasets for health facilities, population exposure, hazard mapping, and vulnerability assessments.
AI4RISK is an AI-driven, One Health risk mapping initiative designed to address fragmented disease risk detection caused by siloed data, complex analytical tools, and limited technical capacity. It was originally developed as an innovation concept under the 2025 WHO LEAD “AI for All” Challenge and, among 76 global submissions, was the only initiative from the WHO European Region to advance to the implementation stage. The project has since evolved into a practical implementation and validation phase, focusing on developing prototype workflows, testing AI-enabled risk modelling approaches, and assessing operational feasibility. It integrates human, animal, and environmental data into a user-friendly platform that generates real-time, actionable risk maps to support early warning, cross-sector coordination, and evidence-based decision-making, particularly at subnational level.
As AI4RISK transitions from concept to piloting and scaling, two countries in the WHO Europe region have been identified to implement the AI4RISK, therefore additional specialized expertise is required to operationalize and refine the solution. This includes strengthening AI/ML models, integrating diverse multi-source datasets, and building robust, interoperable data pipelines. Technical capacity is essential to ensure that the models are reliable, scalable, and aligned with real-world public health use cases, as well as to support validation, continuous improvement, and integration into existing systems.
The UN Volunteer will contribute directly to the implementation and expansion of AI4RISK by supporting the development and testing of AI/ML workflows, enhancing data integration across One Health domains, and assisting in the design and validation of risk models and geospatial outputs. The role will also support prototype development (including at least two application components), system integration, and documentation, while helping translate technical outputs into practical tools for emergency preparedness and decision-making. In addition, the UN Volunteer will contribute to knowledge sharing, technical coordination, and scaling efforts across the WHO European Region.
During the first month of the assignment, the UN Volunteer will work closely with his/her direct supervisor to finalize an agreed-upon work plan. The work plan should outline key objectives and activities and include regular check ins with the supervisor to review progress and receive performance feedback.
The Hub works to enhance the availability, quality, and interoperability of spatial data across the Region. It plays a key role in supporting country offices, technical teams, and response partners by providing timely geospatial analysis and developing shared regional datasets for health facilities, population exposure, hazard mapping, and vulnerability assessments.
AI4RISK is an AI-driven, One Health risk mapping initiative designed to address fragmented disease risk detection caused by siloed data, complex analytical tools, and limited technical capacity. It was originally developed as an innovation concept under the 2025 WHO LEAD “AI for All” Challenge and, among 76 global submissions, was the only initiative from the WHO European Region to advance to the implementation stage. The project has since evolved into a practical implementation and validation phase, focusing on developing prototype workflows, testing AI-enabled risk modelling approaches, and assessing operational feasibility. It integrates human, animal, and environmental data into a user-friendly platform that generates real-time, actionable risk maps to support early warning, cross-sector coordination, and evidence-based decision-making, particularly at subnational level.
As AI4RISK transitions from concept to piloting and scaling, two countries in the WHO Europe region have been identified to implement the AI4RISK, therefore additional specialized expertise is required to operationalize and refine the solution. This includes strengthening AI/ML models, integrating diverse multi-source datasets, and building robust, interoperable data pipelines. Technical capacity is essential to ensure that the models are reliable, scalable, and aligned with real-world public health use cases, as well as to support validation, continuous improvement, and integration into existing systems.
The UN Volunteer will contribute directly to the implementation and expansion of AI4RISK by supporting the development and testing of AI/ML workflows, enhancing data integration across One Health domains, and assisting in the design and validation of risk models and geospatial outputs. The role will also support prototype development (including at least two application components), system integration, and documentation, while helping translate technical outputs into practical tools for emergency preparedness and decision-making. In addition, the UN Volunteer will contribute to knowledge sharing, technical coordination, and scaling efforts across the WHO European Region.
During the first month of the assignment, the UN Volunteer will work closely with his/her direct supervisor to finalize an agreed-upon work plan. The work plan should outline key objectives and activities and include regular check ins with the supervisor to review progress and receive performance feedback.
Task description
Under the direct supervision of PHHE-Technical Officer for Digital Health the UN Volunteer will undertake the following tasks:
• Support the design and prototyping of the AI4RISK platform for AI-driven disease risk mapping
• Support the development and integration AI-based risk models using multi-source data (e.g., climate, satellite, mobility, and epidemiological data)
• Support the building of data pipelines to combine human, animal, and environmental datasets aligned with the One Health approach
• Apply WHO-aligned principles on data governance, ethics, and responsible AI
• Document technical specifications and contribute to reports and presentations
• Support the design and prototyping of the AI4RISK platform for AI-driven disease risk mapping
• Support the development and integration AI-based risk models using multi-source data (e.g., climate, satellite, mobility, and epidemiological data)
• Support the building of data pipelines to combine human, animal, and environmental datasets aligned with the One Health approach
• Apply WHO-aligned principles on data governance, ethics, and responsible AI
• Document technical specifications and contribute to reports and presentations
At Impactpool we do our best to provide you the most accurate info, but closing dates may be wrong on our site. Please check on the recruiting organization's page for the exact info. Candidates are responsible for complying with deadlines and are encouraged to submit applications well ahead.
Before applying, please make sure that you have read the requirements for the position and that you qualify. Applications from non-qualifying applicants will most likely be discarded by the recruiting manager.