Research Fellow in Applied Machine Learning

Core responsibilities:

• Lead the design, development, deployment and evaluation of NeoShield's applied machine-learning systems
• Develop the machine-learning-driven Clinical Decision Support Algorithm for neonatal sepsis
• Create and maintain the real-time ward-level outbreak detection system
• Work on the NeoShield Study, a multi-country project to reduce neonatal mortality from healthcare-associated infections in Zambia and Malawi

This role contributes to reducing neonatal mortality from healthcare-associated infections in Zambia and Malawi by developing machine-learning systems for safer, more targeted infection management in hospitalized newborns.

Required qualifications:

• Postgraduate degree, ideally a doctoral degree, in a relevant discipline (e.g. machine learning, data science, epidemiology or another quantitative field)
• Applied experience in machine-learning with extensive hands-on model development, testing, validation and deployment using real-world datasets in operational environments
• Demonstrated experience in data engineering and ETL workflows required to prepare large, real-world datasets for machine-learning development
• Experience in healthcare settings is not essential

Compensation and benefits:

• Salary range: £45,728-£51,872 per annum pro rata (inclusive of London weighting)
• Annual leave entitlement: 30 working days per year, pro rata for part-time staff
• Discretionary "Wellbeing Days"
• Membership of the Pension Scheme available
• Full-time position (35 hours per week, 1.0 FTE)
• Fixed-term for 24 months with potential for extension subject to funding