Research Assistant in Epidemiology/Statistics

Imperial College London
London
1 year ago
Applications closed

Related Jobs

View all jobs

Research Assistant in Machine Learning for Clinical Trials

Research Assistant/Associate in Data Science and Computational Neuroscience

Data Scientist Assistant

Data science programme lead

Data science programme lead

Lead Clinical Data Science Programmer

This role involves conducting impactful research at the forefront of cardiovascular and metabolic health at the Imperial Centre for Cardiovascular Disease Prevention. Using both FHSC registry data and UK Biobank data, the role focuses investigating both the determinants of cardiovascular disease in patients with familial hypercholesterolaemia (FH) as well as ways to facilitate FH detection in the general population.


You will work with the FHSC Coordinating Centre members at Imperial College to provide data analysis for the registry to investigate the role of metabolic risk factors in relation to cardiovascular disease in patients with FH. Subject to skills and time availability, you will also have the opportunity to conduct machine learning analyses using UK Biobank data to answer research questions related to FH.


We are seeking a motivated and skilled candidate with the following:

A master's degree in epidemiology or statistics, with a foundational background in biological or health sciences. Strong quantitative and analytical skills. The intellectual curiosity to develop their own research questions, leveraging the context of available data.


The opportunity to join the Coordinating Centre for the FHSC Registry, the only global registry for patients with FH, from over 70 countries The opportunity to continue your career at a world-leading institution and be part of our mission to continue science for humanity.Grow your career: Gain access to Imperial’s sector-leading as well as opportunities for promotion and progression Sector-leading salary and remuneration package (including 39 days off a year and generous pension schemes).

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.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.