Postdoctoral Research Assistant in Health Data Sciences

University of Oxford
Oxford
1 day ago
Create job alert

Botnar Research Centre, Windmill Road, Oxford, OX3 7LD 184138 Postdoctoral Research Assistant in Health Data Sciences
Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS)
Grade 7: £39,424 per annum. This is a full time (part time may be considered - minimum 80% FTE), fixed term position for 12 months or until 31st August 2027. The Oxford PHI Lab is seeking a highly motivated data scientist to support our projects on curation and modelling of harmonised health datasets and co-creating publicly available decision-support dashboards and tools to enhance mapping, monitoring, and prediction of global health challenges including mitigating climate-exacerbated global health inequities. This includes a new project funded by the Gates Foundation on real-world data for women’s health. In this role, you will develop analysis plans, ethical protocols, standard operating procedures and similar as required for ongoing and future studies as well as undertake related literature reviews. You will curate and analyse real world health data assets, including data on wider determinants of health e.g. climate. As well as analyse data following pre-specified analysis plan/s and/or approved protocols. In this position you will lead the programming of R/Python packages for the analysis as well as adapt existing and develop new research methodologies and training materials. You will report research findings in the form of conference abstracts at national and international conferences and collaborate in the preparation of research publications, and book chapters. Additionally, you will lead and/or support the drafting of scientific manuscripts, reports to funders and other materials for other audiences based on the results from research studies. You must hold a PhD/DPhil (or be near completion) in epidemiology, public health, applied/medical statistics, bio/medical engineering, health data sciences, health informatics, computer science, clinical artificial intelligence, environmental epidemiology, climate data sciences, remote sensing, earth observation, public health geography, or a similar field. You will have demonstrable advanced skills in programming in R, Python, SQL, and/or similar languages, together with experience in version control e.g., Git. You will be able to demonstrate experience in data visualization and creating digital tools and dashboard using e.g. R Shiny, Power BI, Tableau and/or similar. You must have taught or demonstrated skills in epidemiology study design, analysis, and interpretation and must demonstrate the flexibility to learn new skills and programming languages easily.
Experience in cleaning and analysing health data e.g., electronic health records, and/or satellite-derived data analysis products and GIS data as well as experience in medical large language models and foundation models e.g., through HuggingFace are desirable.

Related Jobs

View all jobs

Senior Genomic Data Scientist - 2 Year FTC, Adult Population Genomics Programme (we have office locations in Cambridge, Leeds & London)

Assistant Professor in Actuarial Data Science (T&R)

Assistant Professor in Statistical Data Science

Assistant Professor in Statistical Data Science

Research Associate in Computational Biology and Machine Learning

Postdoctoral Fellow- Computational Biology and Machine Learning

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

AI Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Changing career into artificial intelligence in your 30s, 40s or 50s is no longer unusual in the UK. It is happening quietly every day across fintech, healthcare, retail, manufacturing, government & professional services. But it is also surrounded by hype, fear & misinformation. This article is a realistic, UK-specific guide for career switchers who want the truth about AI jobs: what roles genuinely exist, what skills employers actually hire for, how long retraining really takes & whether age is a barrier (spoiler: not in the way people think). If you are considering a move into AI but want facts rather than Silicon Valley fantasy, this is for you.

How to Write an AI Job Ad That Attracts the Right People

Artificial intelligence is now embedded across almost every sector of the UK economy. From fintech and healthcare to retail, defence and climate tech, organisations are competing for AI talent at an unprecedented pace. Yet despite the volume of AI job adverts online, many employers struggle to attract the right candidates. Roles are flooded with unsuitable applications, while highly capable AI professionals scroll past adverts that feel vague, inflated or disconnected from reality. In most cases, the issue isn’t a shortage of AI talent — it’s the quality of the job advert. Writing an effective AI job ad requires more care than traditional tech hiring. AI professionals are analytical, sceptical of hype and highly selective about where they apply. A poorly written advert doesn’t just fail to convert — it actively damages your credibility. This guide explains how to write an AI job ad that attracts the right people, filters out mismatches and positions your organisation as a serious employer in the AI space.

Maths for AI Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are a software engineer, data scientist or analyst looking to move into AI or you are a UK undergraduate or postgraduate in computer science, maths, engineering or a related subject applying for AI roles, the maths can feel like the biggest barrier. Job descriptions say “strong maths” or “solid fundamentals” but rarely spell out what that means day to day. The good news is you do not need a full maths degree worth of theory to start applying. For most UK roles like Machine Learning Engineer, AI Engineer, Data Scientist, Applied Scientist, NLP Engineer or Computer Vision Engineer, the maths you actually use again & again is concentrated in a handful of topics: Linear algebra essentials Probability & statistics for uncertainty & evaluation Calculus essentials for gradients & backprop Optimisation basics for training & tuning A small amount of discrete maths for practical reasoning This guide turns vague requirements into a clear checklist, a 6-week learning plan & portfolio projects that prove you can translate maths into working code.