Research Associate in AI and Machine Learning

Imperial College London
London
11 months ago
Applications closed

Related Jobs

View all jobs

Postdoctoral Research Associate in Statistical Genetics and Machine Learning (Fixed Term)

Postdoctoral Research Associate in Statistical Genetics and Machine Learning (Fixed Term)

Postdoctoral Research Associate in Statistical Genetics and Machine Learning (Fixed Term)

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Developing artificial intelligence-enabled electrocardiograms and intracardiac electrograms

Applications are invited for the position of post-doctoral research associate, to work at the National Heart and Lung Institute, Imperial College London. We are a multi-disciplinary team seeking a highly motivated post-doctoral research associate to join a collaborative team of clinicians, basic scientists, and physical scientists working on cardiovascular research, currently funded by a British Heart Foundation Programme Grant. The present post is funded by a UKRI Impact Accelerator Award and an NIHR Biomedical Research Centre grant, both

focused on developing new artificial intelligence (AI) models to apply to electrophysiological signals, in the form of electrocardiograms (ECG) and intracardiac electrograms (EGM).

Through various collaborators worldwide, we have access to large quantities of digital ECGs (>2 million) recorded in clinical settings, in addition to >60000 digital ECGs from the UK Biobank linked to genetic and phenotypic data, which will allow us to train a range of AI-ECG models. We also have access to a large database of intracardiac EGM data collected during invasive catheter ablation procedures, and from collaborators in industry.

The post-doctoral research associate will work with other members of the team to use these datasets to develop new AI-ECG models and AI-EGM models for cardiovascular risk and mortality prediction, and for diagnostic classification tasks, building on our group’s recent success


The post-doctoral research associate will develop new AI-ECG models and AI-EGM models for cardiovascular risk and mortality prediction, and for diagnostic classification tasks. The post holder will work closely with the clinicians, biologists, bioengineers and physical scientists within the multi-disciplinary group


You should have a PhD (or submitted a PhD and awaiting viva), or an equivalent qualification, industrial or commercial experience, in a computational discipline.

*Candidates who have not yet been officially awarded their PhD will be appointed as a Research Assistant within the salary range £43,003 - £46,297 per annum

Experience in computer programming (in particular Python), is essential, and previous experience with AI research is highly desirable.  A background in cardiovascular research is also desirable. You should also have a collaborative approach to research as this role requires working with other groups both within and outside the department. 


Supporting you in developing your career into an independent researcher The opportunity to continue your career at a world-leading institution Sector-leading salary and remuneration package

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.

Jobs

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

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

Careers Jobs

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

Education

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

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.