At a Glance
- Tasks: Join a cutting-edge research project using AI to model heart disease and improve patient care.
- Company: Queen Mary University of London, a leader in digital and data science research.
- Benefits: Competitive salary, generous leave, flexible working, and professional development opportunities.
- Other info: Dynamic research environment with excellent career growth and collaboration opportunities.
- Why this job: Make a real impact in healthcare by developing innovative digital twin models for patient treatment.
- Qualifications: PhD in a numerate field with expertise in machine learning; healthcare data experience is a plus.
The predicted salary is between 37000 - 44000 £ per year.
Applications are invited for a Postdoctoral Research Associate with experience in machine and deep learning to work on a research project funded by the Precision Health, Cardiovascular Devices and Trials Theme, part of a recent NIHR Biomedical Research Centre Award to Barts Health NHS Trust.
The research will involve using the cardiovascular digital twin concept to digitally model three disease states: acute coronary syndromes, valvular heart disease and hypertension. These models will be created by inputting data from available existing and novel wearables, sensing technologies, apps and large data registries. Using this data, digital models will be created to provide personalised patient treatment plans supporting their disease management, predicting disease states/trends and enabling device choice/design by predicting the patient’s response. The role is funded for 3 years in the first instance.
The ideal candidate will have a PhD in a numerate field with expertise in machine and deep learning methods, supported by high quality publications. Experience applying such methods to healthcare data is an advantage.
Appointments at grade 4 through to grade 5 and offers will be made based on level of experience in relation to the duties and expectations of the role and person specification, as outlined in the Job Profile.
The Digital Environment Research Institute (DERI) is Queen Mary’s first University Research Institute, created as part of the university’s ambitious Strategy 2030. University Research Institutes represent a paradigm shift as they are outside the standard Queen Mary faculty structure and collaborate across the university to advance multi-disciplinary research and innovation.
DERI is dedicated to ground-breaking research in digital and data science, including AI. DERI offers an outstanding research environment including a dedicated physical space along with recently purchased high performance computing infrastructure to enable scientific breakthroughs. Further, DERI leads the university’s participation in The Alan Turing Institute, the UK national institute for data science and AI. By tackling grand challenges, DERI will have impact in academia, government, industry and more broadly, society.
At Queen Mary University of London, we believe that a diversity of ideas helps us achieve the previously unthinkable. We continue to embrace diversity of thought and opinion in everything we do, in the belief that when views collide, disciplines interact, and perspectives intersect, truly original thought takes form.
We offer competitive salaries, access to a generous pension scheme, 30 days’ leave per annum (pro-rata for part-time/fixed-term), a season ticket loan scheme and access to a comprehensive range of personal and professional development opportunities. In addition, we offer a range of work life balance and family friendly, inclusive employment policies, flexible working arrangements, and campus facilities including an on-site nursery at the Mile End campus. Queen Mary’s commitment to our diverse and inclusive community is embedded in our appointments processes. Reasonable adjustments will be made at each stage of the recruitment process for any candidate with a disability. We are open to considering applications from candidates wishing to work flexibly.
Postdoctoral Research Associate in Cardiac AI/Digital Twins employer: Queen Mary University of London
Queen Mary University of London is an exceptional employer, offering a vibrant and inclusive work culture that fosters innovation and collaboration in the field of digital and data science. With access to cutting-edge research facilities, competitive salaries, and generous benefits including flexible working arrangements and professional development opportunities, employees are empowered to thrive both personally and professionally. The Digital Environment Research Institute (DERI) provides a unique environment for impactful research, making it an ideal place for those looking to contribute to meaningful advancements in healthcare through AI and digital twins.
Contact Details:
Queen Mary University of London Recruitment Team
StudySmarter Expert Advice🤫
We think this is how you could land Postdoctoral Research Associate in Cardiac AI/Digital Twins
✨Network Like a Pro
Get out there and connect with folks in your field! Attend conferences, workshops, or even local meetups. We all know that sometimes it’s not just what you know, but who you know that can help land that dream job.
✨Show Off Your Skills
Don’t just sit back and wait for opportunities to come to you. Create a portfolio showcasing your projects, especially those related to machine learning and healthcare data. We want to see what you can do, so make it easy for potential employers to find your work!
✨Ace the Interview
When you get that interview, prepare like it’s the final exam! Research the institute, understand their projects, and think about how your skills fit in. We’re looking for candidates who can not only talk the talk but also walk the walk.
✨Apply Through Our Website
Make sure to apply through our website for the best chance at landing the role. We keep everything organised and it shows us you’re serious about joining our team. Plus, it’s super easy to navigate!
We think you need these skills to ace Postdoctoral Research Associate in Cardiac AI/Digital Twins
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to highlight your experience in machine and deep learning, especially as it relates to healthcare data. We want to see how your skills align with the role, so don’t hold back on showcasing relevant projects or publications!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you’re passionate about cardiac AI and digital twins. We love seeing candidates who can connect their personal motivations with our mission at StudySmarter.
Showcase Your Research Experience:Since this role involves cutting-edge research, be sure to detail your previous research experiences. Highlight any specific projects that involved digital modelling or predictive analytics, as these will resonate well with us.
Apply Through Our Website:We encourage you to apply through our website for a smoother application process. It’s the best way to ensure your application gets into the right hands and shows us you’re serious about joining our team!
How to prepare for a job interview at Queen Mary University of London
✨Know Your Stuff
Make sure you brush up on your machine and deep learning knowledge. Be ready to discuss specific algorithms and techniques you've used in your research, especially those related to healthcare data. This will show that you're not just familiar with the theory but can apply it practically.
✨Showcase Your Publications
Prepare to talk about your high-quality publications. Highlight how your work aligns with the cardiovascular digital twin concept and the specific disease states mentioned in the job description. This will demonstrate your expertise and relevance to the role.
✨Understand the Institute's Vision
Familiarise yourself with the Digital Environment Research Institute's goals and its role within Queen Mary University. Being able to articulate how your research fits into their multi-disciplinary approach will impress the interviewers and show your enthusiasm for contributing to their mission.
✨Ask Insightful Questions
Prepare thoughtful questions about the research project and the team dynamics. Inquire about the types of data sources they currently use or plan to integrate. This not only shows your interest but also helps you gauge if the environment is the right fit for you.
