Research Assistant (Fixed Term)

University of Cambridge
Cambridge
1 year ago
Applications closed

Related Jobs

View all jobs

Research Assistant/Associate in Data Science and Computational Neuroscience

Research Assistant in Machine Learning for Clinical Trials

Predoctoral - Data Science Research Assistant

Data Science & Computational Neuroscience Research Assistant

Research Fellow in machine learning and spatial statistics

Research Fellow in Machine Learning and Spatial Statistics, Warwick, UK

The Leverhulme Centre for the Future of Intelligence (CFI), University of Cambridge, is seeking a full-time Research Assistant to contribute to a two-year project focused on developing a benchmark for measuring the ability of Artificial Intelligence (AI) agents on real-world data science tasks. The Research Assistant will join the Kinds of Intelligence team at CFI, a multidisciplinary team comprising expertise in Computer Science, AI, cognitive science, and others. The role is fixed term until 30 September 2026.

CFI is a highly interdisciplinary research centre addressing the challenges and opportunities posed by artificial intelligence (AI), in both the short and long term. CFI is based at the University of Cambridge, with partners in Imperial College London, and UC Berkeley, and close links with industry and policymakers. More information is available at: .

The Research Assistant's main duty will be developing the tasks composing the benchmark, which will be adapted from university-level courses or built from new tasks in collaboration with industry professionals. Further, the research assistant will help with designing the overall structure of the benchmark, determining the grading criteria of the tasks, creating the interface through which the AI agents interact with the tasks, and building baseline agents based on state-of-the-art Large Language Models (LLMs).

Applicants should have, or about to obtain, a postgraduate degree or an undergraduate degree complemented by relevant experience. The degree should be in a discipline relevant to the project, including (but not limited to) computer science, artificial intelligence, statistics, and data science. We expect the successful candidate to be capable to work independently and in collaboration with others, to be organised and able to navigate large amount of information, and to possess strong programming skills (preferably in Python). Experience working with and evaluating LLMs and professional experience in data science would be beneficial but are not required. Some experience in scientific writing is desirable. All applicants should be able to demonstrate strong motivation in line with the goals of CFI and the desire to improve their research skills.

The closing date for applications is midnight (GMT) on Sunday 17 November 2024. Interviews are planned for the end of November 2024/early December 2024, subject to change.

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.