ILRI PhD Graduate Fellowship: Genomic Data Modelling for Public Health Response Using Wastewater Genomic and Clinical Data.

The International Livestock Research Institute (ILRI) seeks to recruit a PhD graduate fellow to work in the Genomic Data Modelling for Public Health Response Using Wastewater Genomic and Clinical Data 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

Advances in wastewater-based epidemiology (WBE) and metagenomic sequencing are transforming how population-level pathogen dynamics and antimicrobial resistance (AMR) are monitored. This project builds on a large-scale longitudinal dataset generated from 30 urban wastewater sampling sites, capturing high-resolution microbial and resistome profiles over time.

While these datasets provide rich biological insights, their true value lies in their ability to inform timely public health action. Integrating genomic signals from wastewater with clinical surveillance data presents a unique opportunity to develop data-driven predictive, genomic-informed models that can detect emerging threats, anticipate outbreaks, and guide intervention strategies.

This PhD will focus on developing genomic data-driven modelling frameworks that translate complex metagenomic and clinical datasets into actionable intelligence for public health response systems.

1. Curate, preprocess, and harmonize large-scale metagenomic (pathogen and resistome) and clinical datasets.
2. Develop genomic-informed predictive models that link pathogen abundance and AMR gene dynamics to public health indicators such as disease incidence/prevalence.
3. Apply and compare AI/ML and statistical modelling approaches (e.g., time-series models, LSTM, Bayesian hierarchical models, random forests).
4. Identify genomic signatures and covariates (environmental, socioeconomic, wastewater-derived, epidemiological factors) that provide early signals of outbreaks or AMR shifts.
5. Model spatiotemporal dynamics of pathogen transmission using integrated environmental and clinical data.
6. Evaluate the public health utility of models, including sensitivity, timeliness, and interpretability for decision-making.
7. Translate modelling outputs into operational insights, including thresholds, alerts, and risk indicators usable by public health agencies.
8. Collaborate with epidemiologists and public health stakeholders to ensure policy relevance and usability of model outputs.
9. Contribute to the development of reproducible analytical pipelines for genomic data modelling.
10. Publish findings in peer-reviewed journals and contribute to policy briefs and technical guidance.

1. Master’s degree in bioinformatics, Computational Biology, Data Science, Genomics, or 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. Experience with model evaluation techniques including cross-validation, performance metrics (RMSE, AUC, precision/recall) and statistical testing.
4. Proficiency with workflow orchestration and scheduling software (e.g Nextflow, Slurm, Snakemake, WDL).
5. Experience with containerization systems such as Docker and Apptainer/Singularity.
6. Proficiency in version control and collaboration Git and Github
7. Experience with genomic or metagenomic data analysis.
8. Demonstrated experience with machine learning or statistical modelling.
9. Understanding of infectious disease dynamics or epidemiological modelling 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/01/2026 should be clearly marked on the subject line of the cover letter.