Research Fellow in Applied Machine Learning

The London School of Hygiene & Tropical Medicine (LSHTM) is one of the world’s leading public health universities. Our mission is to improve health and health equity in the UK and worldwide; working in partnership to achieve excellence in public and global health research, education and translation of knowledge into policy and practice.

The Department of Infectious Disease Epidemiology & International Health is seeking to appoint a Research Fellow to the NeoShield Study, a multi-country project designed to reduce neonatal mortality from healthcare-associated infections in Zambia and Malawi.

The study integrates clinical, microbiological, and data science approaches to generate evidence and tools for safer, more targeted infection management in hospitalised newborns.

Key output involves leading the design, development, deployment and evaluation of NeoShield’s applied machine-learning systems, the machine-learning-driven Clinical Decision Support Algorithm for neonatal sepsis and the real-time ward-level outbreak detection system.

The successful candidate will hold a postgraduate degree, ideally a doctoral degree, in a relevant discipline (e.g. machine learning, data science, epidemiology or another quantitative field), and will have applied experience in machine-learning, with extensive experience of hands-on model development, testing, validation and deployment using real-work datasets in operational environments. Demonstrated experience in data engineering and ETL workflows required to prepare large, real-world dataset for machine-learning development is also essential. Please note experience in healthcare settings is not essential. Further particulars are included in the job description. 

The post is full-time 35 hours per week, 1.0 FTE and fixed-term for 24 months with potential for extension subject to funding. The post is funded by Wellcome Trust and Gates Foundation and is available immediately.

The salary will be on the LSHTM salary scale, Grade 6 in the range £45,728-£51,872 per annum pro rata (inclusive of London weighting). The post will be subject to the LSHTM terms and conditions of service. Annual leave entitlement is 30 working days per year, pro rata for part-time staff. In addition to this there are discretionary “Wellbeing Days”. Membership of the Pension Scheme is available. The post is based in London at LSHTM. 

Applications should be made by clicking the 'Apply' button above. Online applications will be accepted by the automated system until 10pm of the closing date. Any queries regarding the application process may be addressed to jobs@lshtm.ac.uk. Please quote reference EPH-EPIH-2026-01.

The supporting statement section should set out how your qualifications, experience and training meet each of the selection criteria. Please provide one or more paragraphs addressing each criterion. The supporting statement is an essential part of the selection process and thus a failure to provide this information will mean that the application will not be considered. An answer to any of the criteria such as "Please see attached CV" will not be considered acceptable. 

Please note that if you are shortlisted and are unable to attend on the interview date it may not be possible to offer you an alternative date.

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