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- Tasks: Lead the design and development of machine-learning systems for neonatal health.
- Company: Join LSHTM, a top public health university making a global impact.
- Benefits: Competitive salary, 30 days annual leave, wellbeing days, and pension scheme.
- Why this job: Make a real difference in neonatal health using cutting-edge machine learning.
- Qualifications: Postgraduate degree in machine learning or related field with hands-on experience.
- Other info: Full-time role with potential for extension, based in vibrant London.
The predicted salary is between 45728 - 51872 ÂŁ per year.
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.
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.
Research Fellow in Applied Machine Learning in London employer: London School of Hygiene and Tropical Medicine
Contact Detail:
London School of Hygiene and Tropical Medicine Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Research Fellow in Applied Machine Learning in London
✨Tip Number 1
Network like a pro! Reach out to people in your field, especially those connected to LSHTM or similar projects. A friendly chat can open doors and give you insights that might just land you that Research Fellow role.
✨Tip Number 2
Show off your skills! Prepare a portfolio showcasing your machine learning projects, especially any that relate to healthcare or data science. This will help us see your hands-on experience and how you can contribute to the NeoShield Study.
✨Tip Number 3
Practice makes perfect! Get ready for interviews by rehearsing common questions related to applied machine learning and your past experiences. We want to see how you think on your feet and tackle real-world problems.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re serious about joining our mission to improve health equity worldwide. Don’t miss out!
We think you need these skills to ace Research Fellow in Applied Machine Learning in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Research Fellow role. Highlight your experience in machine learning and any relevant projects you've worked on. We want to see how your skills align with the NeoShield Study!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about reducing neonatal mortality and how your background makes you a perfect fit for this position. Let us know what excites you about the project!
Showcase Your Technical Skills: Since this role involves applied machine learning, be sure to showcase your technical skills clearly. Mention specific tools and methodologies you've used in past projects. We love seeing hands-on experience with real-world datasets!
Apply Through Our Website: Don't forget to apply through our website! It’s the best way to ensure your application gets to us directly. Plus, it helps us keep track of all the amazing candidates like you who are interested in making a difference.
How to prepare for a job interview at London School of Hygiene and Tropical Medicine
✨Know Your Machine Learning Stuff
Make sure you brush up on your machine learning concepts and techniques. Be ready to discuss your hands-on experience with model development, testing, and deployment. They’ll want to hear about specific projects you've worked on, so have some examples ready to share.
✨Understand the NeoShield Study
Familiarise yourself with the NeoShield Study and its goals. Knowing how your role as a Research Fellow fits into the bigger picture will show your genuine interest in the project. Think about how your skills can contribute to reducing neonatal mortality and improving infection management.
✨Prepare for Technical Questions
Expect technical questions related to data engineering and ETL workflows. Brush up on your knowledge of preparing large datasets for machine learning. Being able to explain your process clearly will demonstrate your expertise and problem-solving skills.
✨Show Your Passion for Public Health
Even if you don’t have direct healthcare experience, express your enthusiasm for public health and global health research. Share why you’re excited about the potential impact of your work on neonatal health and how it aligns with LSHTM's mission.
