Postdoctoral Research Assistant

Barts Cancer Institute , Queen Mary University London
Central London
1 year ago
Applications closed

Related Jobs

View all jobs

Postdoctoral Research Assistant in Health Data Sciences

Senior Postdoctoral Researcher in Biostatistics: Statistical Machine Learning

Data Scientist

Postdoctoral Researcher in Biostatistics - Statistical Machine Learning

Computer Vision and Artificial Intelligence Engineer

Faculty Fellowship Programme - Data Science - May 2026

About the Role

The post holder will be a core member of the Lamb research group (https://www.nds.ox.ac.uk/team/alastair-lamb), newly established at Barts Cancer Institute after 6 years in Oxford. The individual will have responsibility for developing computational aspects of the SPACE study (#SPACE_Study) centred on use of Visium spatial transcriptomics to interrogate the spatial and evolutionary relationships between prostate cancer cells (https://youtu.be/YdzF0-PFXhc;https://www.nature.com/articles/s41586-022-05023-2;)

About You

The successful candidate will have a PhD in a relevant subject and a proven track record in computational biology and data science, coming from either a biological or computational background. They will be expert in using R to process and analyse genomic data, with additional experience of another scripting language (e.g. Python). Previous experience in prostate cancer research is desirable but not essential. The post holder will have excellent communication and team-working skills and understand the importance of collaboration in team-science. They will be able to demonstrate self-awareness of their limitations and be able to provide examples that demonstrate their willingness to ask for help.

About the Institute / About the Project

Barts Cancer Institute (BCI) is a leading UK cancer institute and a Cancer Research UK Centre of Excellence. It has core funding from Cancer Research UK as part of the City of London Cancer Centre along with the Francis Crick Institute and UCL. BCI is renowned for ground-breaking basic research alongside translational cancer medicine, with strong links to Barts Health, Guy's and St Thomas' (GSTT) and other NHS Foundation Trusts. BCI is committed to supporting and developing future cancer researchers through its extensive postgraduate training programme.

About Queen Mary

At Queen Mary University of London, we believe that a diversity of ideas helps us achieve the previously unthinkable.

Throughout our history, we've fostered social justice and improved lives through academic excellence. And we continue to live and breathe this spirit today, not because it's simply 'the right thing to do' but for what it helps us achieve and the intellectual brilliance it delivers.

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.

Benefits

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.

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.

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.