Research Associate in Modern Statistics, Global Health, and Conservation Ecology

The International Society for Bayesian Analysis; City Of London; On-site; GBP 43,000 - 51,000; Full time

Job summary

A prestigious research organization in London seeks a Research Associate to develop Bayesian statistical tools for crucial global health initiatives. Candidates should have a strong background in modern statistics and prepare to collaborate with top-tier researchers on various impactful projects. The role offers access to unique datasets and mentoring from leading scientists in the field.

Qualifications

• Strong background in computational statistics required.
• Experience with spatio-temporal statistical tools preferred.
• Ability to work in collaborative, multidisciplinary teams necessary.

Responsibilities

• Develop statistical inference tools for global health projects.
• Apply statistical methods to analyze longitudinal datasets.
• Collaborate with leading researchers on joint projects.

Skills

Machine learning

Bayesian Statistics

Statistical inference

Phylogenetic techniques

Education

PhD in Statistics or related field

Tools

R

Python

Job description

Research Associate in Modern Statistics, Global Health, and Conservation Ecology

Jul 24, 2023

Salary Range: £43,093- £50,834 per annum
Fixed Term for initially 12 Months with extension likely
Start date: 1 October 2023 or soon thereafter

This is an exciting opportunity to help lead an ongoing programme of methodological research to tackle pressing global health problems in collaboration with leading international organisations.

The focus of this post is on the development of novel, flexible and computationally tractable spatio-temporal statistical inference tools in Bayesian Statistics and AI, and on their application in three domains. Applications range from HIV deep-sequence phylogenetics within the PANGEA-HIV consortium, to quantification and hotspot mapping of caregiver loss with the Global Reference Group for Children Affected by COVID-19 and in Crises, and species mapping and forecasting using oceanographic and climatological datasets.

You will have access to some of the finest longitudinal datasets in Africa and South America. Post holders will interact with a team of leading researchers. They will receive hands-on training in machine learning and modern statistics, epidemiological, and phylogenetic techniques, and will be mentored by leading scientists, who often publish in some of the top journals of the field.

Your base will be in the Department of Mathematics at Imperial College London, and you will work closely with the Machine Learning & Global Health Network (MLGH), a multi-institution research laboratory with members at Oxford, Imperial College London, University of Copenhagen, and Singapore. Post holders will be reporting directly to Dr Oliver Ratmann (Imperial), and collaborating closely with Professor Seth Flaxman (Oxford), Dr Kate Grabowski (Johns Hopkins), Dr Ettie Unwin (Bristol), Dr Adam Sykulski (Imperial), and Professor Christophe Fraser (Oxford).