Reader in Computer Vision Education

Kings College London
London
3 months ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Machine Learning/Semantic Web

About you

To be successful in this role, we are looking for candidates to have the following skills and experience: Essential criteria 1. PhD in computer science, quantum computing or related field. 2. Outstanding teaching and examining experience of computer science topics at MSc and/or Undergraduate level (including experience designing a module, developing teaching material and designing assessments, as well as contribute to the pastoral care of students). 3. Ability to develop and deliver high and innovative teaching, including flipped classroom and online methods. 4. Significant experience in university governance and academic administration. Desirable criteria 1. Academic planning and strategic development. 2. Academic leadership experience. Downloading a copy of our Job Description Full details of the role and the skills, knowledge and experience required can be found in the Job Description document, provided at the bottom of the page. This document will provide information of what criteria will be assessed at each stage of the recruitment process. ## Further Information We pride ourselves on being inclusive and welcoming. We embrace diversity and want everyone to feel that they belong and are connected to others in our community. We are committed to working with our staff and unions on these and other issues, to continue to support our people and to develop a diverse and inclusive culture at King's. As part of this commitment to equality, diversity and inclusion and through this appointment process, it is our aim to develop candidate pools that include applicants from all backgrounds and communities. We ask all candidates to submit a copy of their CV, and a supporting statement, detailing how they meet the essential criteria listed in the person specification section of the job description. If we receive a strong field of candidates, we may use the desirable criteria to choose our final shortlist, so please include your evidence against these where possible. To find out how our managers will review your application, please take a look at our ‘ [How we Recruit]( pages.

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.