AI Scientist – AI for Medical Imaging

Lyzeum Ltd; Cambridge; Hybrid; GBP 50,000 - 60,000; Full time

Job summary

Lyzeum Ltd, a University of Cambridge spin-out, seeks an AI Scientist for a 6-month position to develop interpretable AI algorithms for medical diagnostics. You'll work on-site at Addenbrooke’s Hospital, contributing to innovative projects that aim to enhance the diagnosis of coeliac disease and other conditions.

Qualifications

• Master’s or PhD in a related field or equivalent experience.
• Experience in image classification and segmentation tasks.
• Track record of publications in relevant conferences or journals.

Responsibilities

• Design and implement machine learning models for analyzing biopsy images.
• Collaborate with clinical teams to ensure relevance and interpretability.
• Contribute to experimental design and documentation.

Skills

Python

PyTorch

Image Classification

Instance Segmentation

Semantic Segmentation

Education

Master’s or PhD in a related field

Tools

NumPy

Pandas

Matplotlib

SciPy

Job description

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CTO @ Lyzeum Ltd & PostDoc @ Cambridge | Developing Interpretable AI for the Diagnosis of Coeliac Disease

AI Scientist – AI for Medical Imaging

Duration: 6 months

Start Date: Summer 2025

Salary: £50,000-£60,000 pro rata (based on experience)

About Us

Lyzeum Ltd is a University of Cambridge spin-out at the forefront of AI-driven medical diagnostics. Building on our recent publication in NEJM AI (https://bit.ly/nejm-ai) and coverage in national media (https://bit.ly/guardian-coeliac), we are pioneering interpretable artificial intelligence algorithms for the diagnosis of small intestinal biopsies. We are nearing clinical trial readiness, with the potential for regulatory approval and clinical deployment.

We are seeking a highly motivated AI scientist to join our team for a 6-month position. As part of the role, you will receive a visiting affiliation with the University of Cambridge and work on-site at Addenbrooke’s Hospital at least two days per week, with flexibility for hybrid working.

Role Overview

You will contribute to the development and refinement of interpretable AI algorithms focused on classification and segmentation tasks. Building on our work in coeliac disease detection, you will help expand our models models to other small intestinal conditions. With access to the world’s largest collection of digitised small intestinal biopsies, you’ll have a unique opportunity to develop and evaluate cutting-edge algorithms at scale.

Key Responsibilities

• Design and implement machine learning models for the analysis of scanned small-intestinal biopsy images

• Collaborate closely with clinical and technical teams to ensure clinical relevance and interpretability of the algorithms

• Contribute to experimental design, model evaluation, and documentation

• Co-author papers for submission to high-impact medical journals.

• Strong proficiency in Python and PyTorch

• Ability to work independently and collaboratively in multidisciplinary teams

Desirable Qualifications

• Master’s or PhD in a related field or equivalent experience

• Experience in image classification, instance segmentation, and semantic segmentation tasks

• A track record of publications in machine learning or medical imaging conferences or journals

• Experience working with large-scale medical datasets

• Experience contributing to open-source GitHub projects and strong proficiency in Python-based data science tools (e.g., NumPy, Pandas, Matplotlib, SciPy)

How to Apply

To apply, please email your CV and a brief cover letter (up to 500 words) outlining your relevant experience and motivation for joining Lyzeum Ltd to [e-mail]. The closing date is 16 June 2025; however, applications will be reviewed on a rolling basis, so early applications are encouraged.

Lyzeum Ltd is committed to building an inclusive team. If you require any adjustments at any stage of the application process, please don't hesitate to contact us at [e-mail].

Seniority level

• Seniority level

  Entry level

Employment type

• Employment type

  Full-time

Job function

• Job function

  Engineering and Information Technology

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