🚨 We are recruiting! Research Fellow in Applied Machine Learning at London School of Hygiene and Tropical Medicine, U. of London!

If you build machine-learning systems and want your work deployed and used in real clinical settings, this could be an excellent next step - whether your background is academia, industry, start-ups, or the public sector.

We are expanding our NeoShield team, a multi-country programme working with neonatal units in Malawi and Zambia to strengthen microbiology systems, improve neonatal sepsis care, detect outbreaks, and optimise antibiotic stewardship using routine clinical and laboratory data.

🔘 The role
• Full-time Research Fellow (Grade 6)
• 24-month contract, with potential for extension
• Salary: £45,728–£51,872 (inclusive of London weighting)
• Based at LSHTM, London (hybrid)
• Funded by the Wellcome Trust and Gates Foundation
• Close collaboration with Zambia National Public Health Institute (ZNPHI) and Malawi Liverpool Wellcome Research Programme

🔘 Why this role is different
This is not a “model in a notebook” position. You will have end-to-end technical oversight of two production ML systems:
• A machine-learning-based Clinical Decision Support Algorithm for neonatal sepsis
• A real-time outbreak detection system

🩺 What you will work on
• Build and validate ML models using longitudinal, sparse, noisy real-world data
• Design features, baselines and alerting logic under operational constraints
• Monitor drift, recalibrate, and refine systems post-deployment
• Build reproducible, well-documented, version-controlled pipelines
• Work directly with clinicians, laboratories and Ministries of Health
• Travel to Malawi & Zambia to support deployment and user testing
• Contribute to publications, open-source outputs and policy-relevant tools

📈 Who this is for
Someone motivated by applied ML with real-world consequences, regardless of sector. Essential experience includes:
• A postgraduate degree (ideally PhD) in ML, data science, statistics, etc.
• Substantial hands-on applied ML experience beyond coursework
• Experience working with temporal or time-series data and data pipelines
• Strong practice in version-controlled code (e.g. GitHub)
• Comfort working independently while collaborating across disciplines

🧫 Why this role matters
Neonatal infections remain a leading cause of newborn mortality globally. NeoShield links laboratory diagnostics, clinical care and machine learning to improve antibiotic decision-making and detect outbreaks in high-burden hospitals. The systems you build will be used in real time, in real wards, with real patients.

👉 How to apply
Applications are open via the LSHTM Careers portal --> https://jobs.lshtm.ac.uk/vacancy.aspx?ref=EPH-EPIH-2026-01