Be part of the next generation of digital health innovators.

 Programme structure

The four-year programme will enable you to embark on a substantial research project, building on a tailored training programme including: 

  • Lectures on the programme’s key themes 

  • Training in research skills, responsible and sustainable innovation, research methods, systematic literature reviews, ethics and integrity, and research impact 

  • Technical training e.g. scientific computing, coding, materials and devices, prototyping and product design 

Students will also benefit from: 

  • Our annual doctoral conference, bringing together current students and expert digital health scientists 

  • Partner ‘sandpits’, providing the opportunity to develop research ideas with real-world impact 

  • Equality, diversity and inclusion (EDI) training and wellbeing sessions 

  • Patient, public involvement and engagement (PPIE) training 

  • A 3-month industry/clinical secondment (Year 1) 

  • Clinical shadowing scheme (Year 2 onwards) 

See below for more detailed information about programme structure and activities:

  • Year 1 of the programme will involve comprehensive training enabling students to develop digital health technologies that have a positive impact on patients and society.

    • Individual Skills Assessment via a competency framework

    • Theme-related training including guest lectures

    • Core training research skills such as: research methods, research integrity and ethics, research impact, responsible and sustainable innovation & digital health challenges.

    • Core technical skills such as: programming, AI & machine learning, materials, devices, prototyping/product design, human-computer interaction/UX and behavioural science.

    • 3 month secondment with an industrial or clinical digital health partner.

    • Selection of the PhD project.

  • From Year 2 onwards, students will focus on their PhD project and will have the opportunity to be involved with centre-led opportunities as outlined below.

    Events: Annual Conference, stakeholder and partner networking and engagement, student presentations and keynotes, annual challenges and competitions (e.g datathon/hackathon), research prizes and awards and project generation sandpits.

    Cohort Building: Journal clubs, writing hubs, partner seminars and networking activities

    Clinical Shadowing

    Transferrable & Commercial Skills Training: Entrepreneurial mindset, medical device regulation, technical standard (HL7 fhir), presentation training, grant writing, project management, leadership and teamwork training

    Specialised technical skills training (including industry led training): Sensor technologies, DSP, data handling/sharing, image processing

    EDI, Sustainability and wellbeing activities: Information seminars (e.g. undertaking a PhD while parenting), resilience training, green computing, open-source technology, reusable code..

    Career Counselling & Career Training activities: Careers workshops with employers, CV clinics, research-teaching nexus training, postdoctoral fellowship support, three minute thesis (3MT) competition.

    Patient and Public Involvement and Stakeholder engagement: PPI training, digital storytelling workshop to help communicate research, media training, public co-production living lab events, policy briefing reporting, digital health hackathons/datathons

The programme will consist of four themes, covering all disease areas:

  • Timely identification of disease strongly influences the outcome and the cost of healthcare, but stretched resources and waiting lists mean new technological solutions are needed to ensure people are diagnosed promptly.

    Example projects:

    • Breath analysis device for cancer-screening

    • Smartphone-based retinal scanning in the home

    • Decision support tools for dementia diagnosis using blood-based biomarkers

  • Efficiency and health economics are at the heart of modern healthcare. New tools and methods can improve patients’ care pathways.

    Example projects:

    • Deep learning models for patient pathway prognostics

    • Drones to support public access defibrillator delivery

    • Point-of-care sensors for patient-specific dosing of drugs

  • Population disease surveillance is crucial to protect people, health systems and the economy from the threat of infectious diseases.

    Example projects:

    • Mobile phone-connected sensors for self-testing of influenza

    • Ultra-sensitive quantum sensors for environmental surveillance of infectious diseases in wastewater

    • Automated assessments of an individual’s risk of disease via web search activity

  • Healthcare generates about 30% of all data globally, and NHS data could create value worth around £9.6 billion each year.

    Individuals also generate their own health data through apps and wearables that are yet to be interoperable and integrated into care records.

    It is vital to create health data systems to allow for secure data sharing.

    Example projects:

    • Data visualisation to mitigate AI automation bias in radiology

    • Data analytics of medical device/software user interaction data for real-world validation and post-market surveillance

    • ECG data classification for improved diagnostics using explainable deep learning