Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Join a pioneering team to develop AI solutions for cancer care and improve patient outcomes.
- Company: Be part of Queen Mary University’s innovative PharosAI initiative, transforming cancer treatment in the UK.
- Benefits: Enjoy flexible working arrangements, comprehensive staff benefits, and a supportive, inclusive environment.
- Why this job: Make a real impact in cancer research while collaborating with top scientists and clinicians.
- Qualifications: PhD and medical degree required, with experience in histopathology and machine learning techniques.
- Other info: This role is part of a larger project with multiple exciting opportunities across various disciplines.
The predicted salary is between 36000 - 60000 £ per year.
About the Role
About the Project
We are seeking a talented and dedicated team of scientists, bioinformaticians and support colleaguesto join the ground-breaking PharosAI initiative – a £43.6M national programme co-led by Queen Mary University of London. PharosAI is set to revolutionise AI-powered cancer care, accelerating the development of breakthrough therapies, advancing clinical applications, and improving access to cutting-edge technology across the UK healthcare and biotech sectors. Read more about the initiative here
This is a unique opportunity to help build a first-of-its-kind cancer AI development ecosystem, democratising access to data, technologies, and AI expertise, while directly contributing to patient care and innovation.
PharosAI offers more than a job—it offers a mission. You\’ll be part of a forward thinking, interdisciplinary team building a federated, secure AI platform designed to support NHS delivery, AI-driven drug discovery, and real-world clinical application. You\’ll also help lead the way in fair, transparent data sharing, patient involvement, and education in AI for healthcare professionals.
This is your chance to contribute to one of the most visionary cancer innovation projects in the UK—and make a real difference. This role is part of multiple exciting roles that we are recruiting into across a variety of disciplines for this project.
About you
For this role you will a have a relevant PhD and medical degree alongside registration with the GMC at Specialist Registrar Garde or below. You will have a recent track record in histopathology and use of machine learning techniques, and appropriate clinical knowledge in oncology and histopathology. You will be innovative, with high standards of accuracy and analysis.
For all our roles we are searching for those who will be passionate about contributing to cutting-edge cancer research and AI-driven innovation, with either or both capable technical backgrounds and collaborative mindsets, and a commitment to delivering or supporting excellence in research and the impact this can have on our society.
The project will be based at the Barts Cancer Institute, part of the Faculty of Medicine and Dentistry.
About the Institute
The Barts Cancer Institute (BCI) is a Cancer Research UK Centre of Excellence whose work aims to transform the lives of those with and at risk of cancer through innovative research in the laboratory, in patients and in populations. BCI is internationally renowned in many areas of cancer research, and it combines ground-breaking basic research with the expertise of clinicians and clinician scientists. BCI is committed in supporting and developing future cancer researchers through its extensive postgraduate training
About Queen Mary
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 a range of work life balance and family friendly, inclusive employment policies, flexible working arrangements, and campus facilities in addition to comprehensive staff benefits, found here
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.
*As part of the application process, you will be required to answer specific questions. Depending on the number of applications received, we may do an initial shortlisting process based on this criterion only.
Closing Date
27/07/2025, 23:55
Clinical Research Fellow - PharosAI employer: Barts Cancer Institute, Queen Mary University London
Contact Detail:
Barts Cancer Institute, Queen Mary University London Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Clinical Research Fellow - PharosAI
✨Tip Number 1
Familiarise yourself with the PharosAI initiative and its goals. Understanding the project's mission to revolutionise AI-powered cancer care will help you articulate your passion for the role during interviews.
✨Tip Number 2
Network with professionals in the field of oncology and AI. Attend relevant conferences or webinars to connect with potential colleagues and learn more about the latest advancements, which can give you an edge in discussions.
✨Tip Number 3
Prepare to discuss your experience with machine learning techniques in histopathology. Be ready to provide specific examples of how you've applied these skills in previous roles, as this will demonstrate your suitability for the position.
✨Tip Number 4
Showcase your collaborative mindset by highlighting any interdisciplinary projects you've worked on. Emphasising your ability to work within diverse teams will align well with the values of the PharosAI initiative.
We think you need these skills to ace Clinical Research Fellow - PharosAI
Some tips for your application 🫡
Understand the Role: Read the job description thoroughly to understand the specific requirements and responsibilities of the Clinical Research Fellow position. Highlight your relevant experience in histopathology and machine learning techniques.
Tailor Your CV: Customise your CV to reflect your qualifications, particularly your PhD and medical degree. Emphasise your recent track record in oncology and any innovative projects you've been involved in that align with the PharosAI initiative.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for cancer research and AI-driven innovation. Mention how your skills and experiences make you a perfect fit for the interdisciplinary team at PharosAI.
Prepare for Specific Questions: Be ready to answer specific questions as part of the application process. Think about how your background and expertise can contribute to the goals of the PharosAI project and prepare examples that demonstrate your collaborative mindset.
How to prepare for a job interview at Barts Cancer Institute, Queen Mary University London
✨Showcase Your Passion for Cancer Research
Make sure to express your enthusiasm for cutting-edge cancer research and AI-driven innovation. Share specific examples of how your previous work aligns with the mission of PharosAI and how you envision contributing to this transformative project.
✨Demonstrate Your Technical Expertise
Be prepared to discuss your relevant PhD and medical qualifications in detail. Highlight your experience in histopathology and machine learning techniques, and be ready to explain how these skills can be applied to the role and the broader goals of the project.
✨Emphasise Collaboration and Interdisciplinary Work
Since the role involves working within a diverse team, illustrate your collaborative mindset. Provide examples of past experiences where you successfully worked with interdisciplinary teams, showcasing your ability to communicate effectively and contribute to shared goals.
✨Prepare for Questions on Data Sharing and Ethics
Given the focus on fair and transparent data sharing, be ready to discuss your views on ethical considerations in AI and healthcare. Familiarise yourself with current debates in the field and think about how you would approach these issues in your work at PharosAI.
