Mapping DNA damage and genome replication in malaria parasites with artificial intelligence and long-read sequencing

University of Cambridge
Cambridge
1 year ago
Applications closed

Related Jobs

View all jobs

Postdoctoral Research Assistant in Health Data Sciences

DataOps Engineer – Data Science Operations

Applications are invited for a fully-funded 4-year PhD studentship based in the Department of Pathology at the University of Cambridge under the supervision of Dr Michael Boemo starting October 2025.

Malaria parasites replicate their genomes very differently to human cells, making genome replication an attractive therapeutic target for antimalarial drugs. The purpose of this research is to develop artificial intelligence software that leverages the power of long-read DNA sequencing to determine the genomic loci of DNA damage caused by these drugs and how this damage changes the movement of replication forks throughout the genome.

The student will have the opportunity to learn, or improve upon, the development of artificial intelligence for translational research in a supportive and collaborative environment.

More information about the Boemo Group is available at and .

Funding* will cover the student's stipend at the current Research Council rate and University Fees. The studentship will be funded for four years from October 2025. *The studentships are available to students who qualify for UK Home fees.

Applicants should hold (or expect to obtain) the equivalent of a UK 2.1 or higher in an undergraduate honours or Masters degree in a relevant subject. The studentship is open to those eligible for the Home rate of University fees.

Fixed-term: The funds for this post are available for 4 years in the first instance.

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.