A Research Assistant in Machine Learning for Global or Environmental Health is invited to join the Oxford Martin School Informal Cities programme at the Nuffield Department of Women's & Reproductive Health.
The Oxford Martin School programme on Informal Cities starts from the premise that understanding informal cities, and the millions of lives lived within them, is fundamental to meeting the Sustainable Development Goals (SDGs). It also recognises that the diverse and dynamic nature of informal cities creates a huge challenge for data collection and analysis. The programme will concentrate on three cities, one in Africa, one in India and one in Latin America.
The role is expected to lead machine learning related analyses which will contribute to the health-related research of the Informal Cities programme, in collaboration with other team members and collaborators. You will be involved in designing, implementing and developing existing and new methodologies/algorithms/modelling, and provision of accurate and novel analytical data solutions to a range of current and planned global/environmental health projects within the team and other related projects of the Informal Cities programme.
You will work within a team with expertise in medicine, epidemiology, urban/geospatial science, and machine intelligence to use 'Big Data' to improve health and clinical care, in the context of informal cities. Data sources include various open source datasets as well as those obtained from our collaborators.
You will hold an MSc or above (or near completion) in Machine Learning, Data Science, Health Analytics, Mathematics, Statistics, or other related disciplines with strong quantitative elements. You will have proven knowledge and applications of a wide range of Machine Learning algorithms as well as excellent programming skills using packages such as Python/R. You will have good knowledge of the evolving Machine Learning literatures/advanced topics, a proven interest or experience in applying Machine Learning in health services/global health/environmental health research and meticulous attention to high levels of accuracy. The initiative to work autonomously and an ability to work well alone and within a multi-disciplinary team, excellent organisational and time management skills to meet deadlines and drive efficiencies as well as excellent written and verbal communication skills in English are also essential for this role.
This position is full-time and fixed-term until 30 September 2022. The role is a Grade 6: £29,176 - £34,804 with a discretionary range to £38,017 p.a. Applications for flexible working arrangements are welcomed and will be considered in line with business needs.
Applications for this vacancy are to be made online (vacancy ID 146858). You will be required to upload a CV and supporting statement as part of your online application.
The closing date for applications is 12.00 noon on Monday 27 July 2020. Interviews are expected to take place on Thursday 13 August 2020.