PhD studentship in Neonatal Monitoring and Data Analytics

The post is joint appointment between the University of Cambridge Departments of Engineering and Paediatrics

Start Date: 17 April 2026

Application Deadline: 14 January 2026

Funding Duration: 3 years (Home fee rate only)

Project Overview

Neonatal intensive care (NICU) presents unique challenges in monitoring vital signs while supporting developmental and psychological needs of preterm infants and their families. Current wired monitoring systems create physical and emotional barriers, limiting parental contact and increasing stress. Evidence shows that improving physical contact through wireless monitoring can enhance breastfeeding rates, reduce hospital stay, and improve neurodevelopmental outcomes. The collaboration between the Department of Paediatrics and Engineering has been exploring novel continuous monitoring systems to support a more holistic approach to care. This has included non-contact monitoring specifically designed for use in the unique setting of NICU and uses an RGB-D (red-green-blue-depth) camera to monitor the infant. The cameras contain a visible light sensor (RGB), and a depth sensor (D). The successful applicant will join the team exploring data science and AI for neonatal care. Areas of work will include:

• Development of algorithms for real-time signal processing and anomaly detection,
• Applying big data analytics and deep learning to NICU datasets for predictive modelling of clinical outcomes,
• Investigating integration of multimodal data streams to support decision-making and improve patient care.

What we are looking for:

Candidates should have a first or upper second-class degree in engineering, computer science, or a related discipline. A demonstrable interest in medical technology and healthcare innovation or one of the following: wireless systems, embedded electronics, signal processing, machine learning, or big data analytics. is essential, as is an interest in working with health-care related data. Applicants with relevant research experience, gained through Master's study or laboratory work, are strongly encouraged to apply. Motivation, creativity and intellectual independence are desirable, as are good communication skills and the ability to work collaboratively.

Informal enquiries about the project can be addressed to:

Prof. Joan Lasenby: jl221@cam.ac.uk
Dr Kathryn Beardsall: kb274@cam.ac.uk

Studentship Details

The funding for this post is available for 3 years. Funding covers the student's stipend and tuition fees at the Home rate. Due to the amount of funding available, we can only offer this studentship to students who are eligible for the Home fee rate.

Full funding details can be found on the Postgraduate Funding Search: https://www.student-funding.cam.ac.uk/fund/phd-studentship-continuous-monitoring-and-data-analytics-in-neonatal-care-2025

How to Apply

Applications must be submitted via the University Applicant Portal.

• Apply for the PhD in Paediatrics and specify the project title: "Continuous Monitoring and Data Analytics in Neonatal Care".
• It is not necessary to contact potential supervisors before applying.
• You also do not need to upload a research proposal as the project is already set - this section can be used to give additional supporting information for your application, or a blank proposal can be uploaded.
• Application Deadline: 14 January 2026.

For informal enquiries, please contact: Prof. Joan Lasenby jl221@cam.ac.uk and/or Dr Kathryn Beardsall kb274@cam.ac.uk

Questions about the application process should be addressed to the Postgraduate Administration team: clusterPG@medschl.cam.ac.uk

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.

Please quote reference RP48204 on your application and in any correspondence about this vacancy.

Key information

Department/location

Department of Paediatrics

Reference

RP48204

Category

Studentships

Date published

9 December 2025

Closing date

14 January 2026