Smart Medical Imaging

EPSRC Centre for Doctoral Training

Available Projects

About Us

The School of Biomedical Engineering and Imaging Sciences at King’s College London is launching an exciting PhD Programme in Surgical and Interventional Engineering. The Doctoral Training Programme (DTP) will train and nurture the next generation of surgical and interventional engineering scientists, researchers, innovators and industry leaders.

The new PhD student cohort will bring together the brightest minds across engineering specialties (e.g. biophotonics, machine learning, medical imaging, computer vision, mechatronics, data sciences, robotics, precision engineering, modelling, user experience and artificial intelligence) to address key challenges faced in the UK and globally by surgeons, interventional specialists and associated teams. 

  • The programme benefits from a unique environment of a world-leading, research-focused university with facilities embedded within a top-rated teaching hospital, St Thomas’ Hospital.
  • The programme has strong links with key MedTech industrial partners nurturing the clinical translation of the research.

Training Programme

Our training programme links and complements that of the EPSRC Centre for Doctoral Training (CDT) in Smart Medical Imaging at King’s College London and Imperial College London. However, students on the PhD programme in Surgical and Interventional Engineering will not follow the same 1 + 3 pathway as CDT students, instead starting their PhD research projects directly. All DTP students will be based at King's College London.

Who Should Apply?

Applications are invited from candidates with interest in multi-disciplinary research and training in a surgical and interventional engineering related domain (instrumentation technologies, and navigation technologies), and a 1st class or upper second degree in relevant engineering subjects such as mathematics, optics, computer science, artificial intelligence, physics, mechanical engineering, electronic and electrical engineering.

Funding

Each studentship is funded for 3.5 years, including tuition fees, a tax-free stipend of £16,777 per year and a generous consumables budget. 

  • Only home UK or EU/EEA candidates fulfilling the 3-year UK residency requirement are eligible for the DTP studentships. EU/EEA applicants are only eligible for a full studentship if they have lived, worked or studied within the UK for 3 years prior to the funding commencing.

How to Apply

Details of the admissions process are available on our How to apply page. The closing date for applications is 5pm on Monday 24th June 2019. 

Available projects

Industry projects

  • DTP_SIE_04 - Intra-operative probe design and image processing optimisation with deep learning for in-vivo and ex-vivo detection of cancerous tissue

Smart Sensors and Actuators research area

  • DTP_SIE_01 - Multi-transmit technology for optimal magnetic resonance imaging of patients with deep brain electrodes
  • DTP_SIE_08 - Interoperable three-dimensional medical device tracking with a fibre-optic ultrasound transmitter
  • DTP_SIE_14 - Development of affordable minimally invasive endoscopic surgical tools for resource constrained operating rooms

Computer-Assisted Interventions research area

  • DTP_SIE_02 - Computational endomicroscopy for background-free neurosurgical optical biopsy
  • DTP_SIE_04 - Intra-operative probe design and image processing optimisation with deep learning for in-vivo and ex-vivo detection of cancerous tissue (Industry project)
  • DTP_SIE_05 - Device safety and visualisation in MRI guided cardiac catheterisation
  • DTP_SIE_09 - Computer-Assisted Planning for Cervical Needle Injection
  • DTP_SIE_10 - MRI-guidance for improved ventricular tachycardia ablation

Computational Modelling research area

  • DTP_SIE_03 - Image-based computational system for guiding ablation treatment of atrial arrhythmias
  • DTP_SIE_06 - Predicating ablation volume for laser interstitial thermal therapy (LiTT) for computer-assisted planning of minimally invasive neurosurgery
  • DTP_SIE_07 - Improving the diagnosis and treatment of peripheral artery disease using computational modelling
  • DTP_SIE_11 - Intra-operative Planning Software for Congenital Cardiac Surgery
  • DTP_SIE_12 - Optimisation of pre- and post-procedural patient assessment for transcatheter mitral valve replacement using personalised computer modelling
  • DTP_SIE_13 - Patient-Specific Modelling of Atrial Fibrillation Incorporating Atrial Anisotropy and Fibrosis