Job Description

Job number

ENG03711

Faculties

Faculty of Engineering

Departments

Department of Bioengineering

Salary or Salary range

49,017 - 57,472 per annum

Location/campus

White City Campus - On site only

Contract type work pattern

Annualised Hours - Full time Fixed term

Posting End Date

9 Nov 2025

About the role

------------------


The role will engage in cutting-edge translational research that develops computational models for predicting outcomes in cardiac diseases. This includes a machine learning model to rule out heart attacks in the emergency room, which has the potential to translate to large savings for healthcare systems in the world, and computational modelling to assist in selecting the most suitable patients for fetal heart interventions performed at 2 centres in Europe. The candidate will work with a team of AI experts as well as skilled clinicians to deliver the research, and will have the chance to utilize unique large datasets.

What you would be doing

---------------------------


You will be conducting research in two areas. First, you will refine and develop a machine learning model for rapidly ruling out heart attacks in the emergency room (ER). More than a million patients present to the ER in the UK suspecting a heart attack, but only 20% actually have it. The rest are typically retained for long durations in the hospital for further monitoring, but this saps substantial hospital resources for the already burdened NHS. Our model will rapidly and safely rule out cases to avoid the retention to conserve hospital resources. You will work with a team of AI experts and cardiologists to refine the model, based on the NIHR Health Informatics Collaborative large dataset, particularly on imputation modelling to address missing data and uneven data collection across different centres. Second, based on our recent deep learning biomechanics modelling work, you will perform cardiac biomechanical modelling to evaluate fetal heart function, to refine patient selection criteria for a fetal heart intervention, fetal aortic valvuloplasty. This intervention is a minimally invasive, catheter-based intervention to alter the development process of a fetal baby's heart to help it avoid malformation at birth. Currently, patient selection is insufficiently accuracy, our preliminary modelling work suggest that biomechanics modelling can improve this. You will work with clinicians across Europe to test your algorithm. You will be responsible for liaising with internal and external collaborators on data collation, perform model development and testing, and collecting feedback on results. There are ample opportunities to network with highly skilled AI experts and clinicians. You will also have the opportunity to co-mentor undergraduate, Masters and/or PhD students. You are further expected to publish findings, and help attract funding.

What we are looking for

---------------------------


o PhD in Computer Science, Computational Bioengineering, Mechanical or Electrical Engineering o Excellent coding skills o Preferably a familiarity with machine learning and deep learning models, as well as a familiarity with cardiology. o Ability to work well in a team, and coordinate team research o Ability to mentor junior researchers o Highly driven and proactive worker with a passionate for the academics.

What we can offer you

-------------------------


o The opportunity to continue your career at a world-leading institution and be part of our mission to continue science for humanity. o Grow your career: gain access to Imperial's sector-leading dedicated career support for researchers as well as opportunities for promotion and progression. o Sector-leading salary and remuneration package (including 39 days off a year and generous pension schemes). o Be part of a diverse, inclusive and collaborative work culture with various staff networks and resources to support your personal and professional wellbeing.

Further information

-----------------------


This is a full-time, fixed term post for 30 months. If you require any further details about the role, please contact: Choon Hwai Yap - c.yap@imperial.ac.uk.

Available documents

-----------------------


Attached documents are available under links. Clicking a document link will initialize its download.

Please note that job descriptions are not exhaustive, and you may be asked to take on additional duties that align with the key responsibilities mentioned above.

We reserve the right to close the advert prior to the closing date stated should we receive a high volume of applications. It is therefore advisable that you submit your application as early as possible to avoid disappointment.



If you encounter any technical issues while applying online, please don't hesitate to email us at support.jobs@imperial.ac.uk. We're here to help.


#

About Imperial

Welcome to Imperial, a global top ten university where scientific imagination leads to world-changing impact.



Join us and be part of something bigger. From global health to climate change, AI to business leadership, here at Imperial we navigate some of the world's toughest challenges. Whatever your role, your contribution will have a lasting impact.



As a member of our vibrant community of 22,000 students and 8,000 staff, you'll collaborate with passionate minds across nine London campuses and a global network.



This is your chance to help shape the future. We hope you'll join us at Imperial College London.


#

Our Culture

We work towards equality of opportunity, to eliminating discrimination, and to creating an inclusive working environment for all. We encourage applications from all backgrounds, communities and industries, and are committed to employing a team that has diverse skills, experiences and abilities. You can read more about our commitment on our webpages.



Our values are at the root of everything we do and everyone in our community is expected to demonstrate respect, collaboration, excellence, integrity, and innovation.