Application Guide

How to Apply for Research Fellow in Applied Machine Learning

at London School of Hygiene and Tropical Medicine

🏢 About London School of Hygiene and Tropical Medicine

The London School of Hygiene & Tropical Medicine is a world-leading center for research and postgraduate education in public and global health, with a strong focus on reducing health inequalities. What makes LSHTM unique is its direct impact on global health policy and practice through applied research, particularly in low-resource settings. Working here means contributing to life-saving research that addresses critical global health challenges.

About This Role

This Research Fellow role involves leading the development and deployment of machine-learning systems for the NeoShield project, specifically focusing on neonatal sepsis clinical decision support and real-time outbreak detection in Zambia and Malawi. The role is highly impactful because it directly addresses neonatal mortality from healthcare-associated infections in resource-limited settings through applied ML solutions that will be implemented in real clinical environments.

💡 A Day in the Life

A typical day might involve collaborating with epidemiologists and clinicians to refine ML model requirements, developing and testing algorithms using real neonatal health data from Zambia and Malawi, and working on data engineering pipelines to prepare messy clinical data for analysis. You'd likely participate in project meetings with international partners and contribute to documentation for both technical and clinical audiences.

🚀 Application Tools

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Interview Prep

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🎯 Who London School of Hygiene and Tropical Medicine Is Looking For

  • Has a PhD in machine learning, data science, epidemiology or related quantitative field with demonstrated experience deploying ML models in operational healthcare environments
  • Possesses extensive hands-on experience with the full ML pipeline: development, testing, validation and deployment using real-world clinical datasets
  • Has strong data engineering skills including ETL workflows for preparing large, real-world healthcare datasets for ML applications
  • Shows genuine interest in global health applications, particularly maternal and child health in low-resource settings like Zambia and Malawi

📝 Tips for Applying to London School of Hygiene and Tropical Medicine

1

Explicitly quantify your experience with real-world ML deployment in healthcare settings - mention specific projects, datasets, and clinical impact

2

Highlight any experience working with healthcare data from low-resource settings or global health contexts

3

Demonstrate understanding of both the technical ML requirements AND the clinical context of neonatal sepsis

4

Show familiarity with LSHTM's global health research approach by referencing specific projects or publications from their maternal and child health research groups

5

Provide concrete examples of your ETL workflow experience with messy, real-world healthcare datasets

✉️ What to Emphasize in Your Cover Letter

['Your experience deploying ML models in operational healthcare environments (not just research settings)', "Specific examples of working with real-world clinical datasets and the challenges you've overcome", 'Your understanding of the clinical context of neonatal sepsis and healthcare-associated infections', "Why you're specifically interested in applying ML to global health challenges in low-resource settings"]

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → The NeoShield Study specifically - its goals, partners, and current progress
  • → LSHTM's work in maternal and child health in sub-Saharan Africa
  • → The clinical context of neonatal sepsis and current diagnostic/treatment challenges in low-resource settings
  • → LSHTM's approach to interdisciplinary research combining epidemiology, statistics, and data science

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Walk us through your experience deploying an ML model in a real clinical environment - what challenges did you face?
2 How would you approach developing a clinical decision support algorithm for neonatal sepsis in resource-limited settings?
3 Describe your experience with ETL workflows for messy healthcare data - what specific tools and approaches have you used?
4 How do you ensure ML models remain effective and safe when deployed in dynamic clinical environments?
5 What interests you specifically about working on the NeoShield project in Zambia and Malawi versus other ML healthcare applications?
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⚠️ Common Mistakes to Avoid

  • Focusing only on theoretical ML knowledge without demonstrating real-world deployment experience
  • Treating this as a generic ML role without showing specific interest in global health applications
  • Failing to demonstrate understanding of the challenges of working with healthcare data from low-resource settings

📅 Application Timeline

This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.

Typical hiring timeline:

1

Application Review

1-2 weeks

2

Initial Screening

Phone call or written assessment

3

Interviews

1-2 rounds, usually virtual

✓

Offer

Congratulations!

Ready to Apply?

Good luck with your application to London School of Hygiene and Tropical Medicine!

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