Postdoctoral Researcher in the topological data analysis of lung and kidney cancer

University of Oxford
Oxford
1 year ago
Applications closed

Related Jobs

View all jobs

Postdoctoral Researcher in Machine Learning analysis of MRI

Postdoctoral Researcher in Machine Learning analysis of MRI

Postdoctoral Researcher in Machine Learning analysis of MRI

Postdoctoral Researcher in Machine Learning analysis of MRI

Postdoctoral Researcher in Computational Neuroanatomy & Artificial Intelligence

Postdoctoral Researcher in Computational Neuroanatomy and Artificial Intelligence

Oxford Ludwig Institute for Cancer Research, Old Road Campus Research Building, Roosevelt Drive, Headington, Oxford, OX3 7DQ We have an exciting opportunity for a Postdoctoral Researcher in the topological data analysis of lung and kidney cancer. Based at the Oxford Ludwig Institute for Cancer Research, you will be supervised by Professor Helen Byrne, together with Professor Heather Harrington (Mathematical Institute) and Dr Erik Sahai (Crick Institute, London). You will be responsible for managing your own academic research and administrative activities. This involves small scale project management, to co-ordinate multiple aspects of work to meet deadlines. You will adapt existing and develop new research methodologies and materials, and prepare working theories and analyse qualitative and/or quantitative data from a variety of sources, reviewing and refining theories as appropriate. Other duties will include presenting papers at conferences or public meetings and representing the research group at external meetings and seminars. It is essential that you hold a PhD/DPhil (or close to completion) in mathematics, data science, statistics, computer science or a related discipline. You will possess sufficient knowledge of data analysis and/or topological data analysis to work within established research programmes of the Ludwig Institute for Cancer Research, the Mathematical Institute’s Wolfson Centre for Mathematical Biology and its Centre for Topological Data Analysis, and the Crick Institute. It is essential you have experience of computing and working with data, and an enthusiasm for learning about the growth and treatment of cancer.

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.