The International Livestock Research Institute (ILRI) seeks to recruit a PhD graduate fellow to work in the Epidemiology, Agentic AI, and Integrated Wastewater Genomic Surveillance project. The successful fellow will be hosted by the Health program at ILRI.                                                                                                   

The International Livestock Research Institute (ILRI) works to improve people’s lives in low- and middle-income countries through livestock science that contributes to equitable and resilient livestock systems to deliver food systems transformation with climate and environmental benefits. It is the only one of 15 CGIAR research centers dedicated entirely to animal agriculture research for the developing world. Co-hosted by Kenya and Ethiopia, it has regional or country offices and projects in East, South and Southeast Asia as well as Central, East, Southern and West Africa. www.ilri.org

The Health program’s goal is to enhance the health and welfare of farmed livestock, improve human well-being, and protect the shared environment by strengthening control measures for animal and agriculture-associated health risks. The program adopts a One Health approach, recognizing the interconnectedness of human, animal, and environmental health. By addressing disease risks at this interface, the program aims to prevent the spread of zoonotic and emerging infectious diseases that threaten public health and food security. Read more here: https://www.ilri.org/index.php/research/themes/health

The position

ILRI and the Genomic Pathogen Analysis Platform (GPAP) at the Technical University of Denmark (DTU) are advancing next-generation approaches for pathogen surveillance by integrating genomics, epidemiology, and artificial intelligence. A key focus is the development of agentic AI tools capable of automating complex analytical workflows, from metagenomic data processing to actionable public health insights.

Building on a large-scale wastewater-based epidemiology (WBE) initiative with longitudinal metagenomic sequencing data from 30 urban sites, this PhD project will integrate environmental genomic data with clinical surveillance data. The project will leverage GPAP’s infrastructure to develop AI-driven, semi-autonomous (“agentic”) analytical pipelines and interactive dashboards that translate complex data streams into real-time decision-support tools for public health systems.

Terms of reference

  1. Lead the harmonization and integration of clinical infectious disease and AMR datasets with wastewater metagenomic data.
  2. Map and align spatial and temporal dynamics between sewer catchment populations and health facility data.
  3. Conduct epidemiological and statistical analyses to identify associations between wastewater-derived pathogen/AMR signals and clinical outcomes.
  4. Collaborate in the design and application of agentic AI tools within GPAP to:
    1. Automate data ingestion, cleaning, and analysis workflows
    2. Detect anomalies, trends, and early warning signals
    3. Generate interpretable summaries for public health users
  5. Contribute to the development of interactive dashboards and visualization tools for real-time surveillance, including:
    1. Pathogen and AMR trend monitoring
    2. Outbreak early warning indicators
    3. Spatial risk mapping
  6. Evaluate the predictive performance and operational utility of GPAP tools in real-world public health scenarios.
  7. Engage with public health stakeholders to co-develop user-centered outputs and ensure policy relevance.
  8. Contribute to implementation frameworks for integrating WBE and AI-enabled analytics into national surveillance systems.
    1. Produce scientific publications, policy briefs, and technical documentation.

Minimum requirements for the ideal candidate

  1. Master’s degree in Epidemiology, Public Health, Global Health, Biostatistics, or a related field.
  2. Proficiency in Python and/or R programming for data manipulation and statistical analysis, including libraries such as pandas, scikit-learn, PyTorch or Tensorflow.
  3. Strong experience in epidemiological analysis and surveillance systems.
  4. Proficiency with cloud platforms (AWS) and deployment of AI/ML models.
  5. Experience with agentic frameworks
  6. Proficiency in version control and collaboration Git and Github
  7. Familiarity with infectious disease epidemiology and AMR.
  8. Exposure to data visualization tools (e.g., R Shiny, Dash, Tableau, Power BI) is desirable.
  9. Interest in or experience with AI/ML applications in public health is an advantage.

Location: ILRI Kenya.

Duration: 3 Years.

Terms of appointment and stipend: ILRI will offer a competitive stipend to cover living expenses in the project location, medical cover, tuition costs and research expenses. The successful candidate will be supervised jointly by ILRI scientists and an academic supervisor.

All applications MUST include the following (applications without the below documents will not be considered):

  • a cover letter expressing their interest in the fellowship position and what they can bring to the fellowship.
  • CV including names and addresses (including telephone and email) of three referees who are knowledgeable about the candidate’s professional qualifications and work experience.
  • academic qualification certificates/transcripts.

Applications should be made to the Senior Manager, Capacity Development, through our recruitment portal http://ilri.simplicant.com/ on or before 03/06/2026. The position title and reference number ILRI PhD GF/Health/02/2026 should be clearly marked on the subject line of the cover letter.

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.