Research Assistant (Ali Group) (Fixed Term)

University of Cambridge
Cambridge
1 year ago
Applications closed

Related Jobs

View all jobs

Research assistant in natural language processing for accessible science

Research Assistant/Associate in Data Science and Computational Neuroscience

Data Science & Computational Neuroscience Research Assistant

Predoctoral - Data Science Research Assistant

Research Fellow in machine learning and spatial statistics

Research Associate in Natural Language Processing (INTERNAL ONLY)

The Ali group invites applications for a Research Assistant. Our research aims to understand how the multicellular structure of breast tumours determines relapse, metastasis, and therapeutic response. We characterise human breast tumour ecosystems using highly multiplexed tissue analysis via imaging mass cytometry and sophisticated image processing. Quantitative analysis of these data is used to understand tumours as complex, dynamic heterocellular ecosystems1-3.

We are assembling tissue imaging datasets of unprecedented depth and size in both observational studies and clinical trials. This will enable us to unmask spatial phenotypic traits that emerge during disease progression and treatment, and to identify those that drive prognosis and therapeutic response.

The suitable candidate should have an MSc degree in computational biology degree or equivalent, with machine learning and artificial intelligence background. Previous experience in cancer research and immunology would be advantageous but is not essential.

As a member of our team, you would contribute to cutting-edge translational research designed to unravel the complex multicellular ecosystems that drive breast cancer. The position would suit a candidate with a strong interest in cancer research.

Once an offer of employment has been accepted, the successful candidate will be required to undergo a basic disclosure (criminal records check) check and a security check.

We welcome applications from individuals who wish to be considered for part-time working or other flexible working arrangements.

We particularly welcome applications from women and /or candidates from a BME background for this vacancy as they are currently under-represented at this level in our department.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.

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.