Engineering science for health studentships

The Engineering Science for Health (ESH) CDT explores research in engineering, mathematics and the physical sciences with applications to Healthcare technologies. The CDT is highly multidisciplinary and includes postgraduate research students with backgrounds in chemistry, physics, biosciences, engineering, computer science and mathematics.

Our research is divided into four main research themes which are all focussed on healthcare applications:

1. Sensors and imaging
2. Biological and molecular systems
3. Computational methods and modelling
4. Patient focussed technologies.

A range of PhD research projects will be offered across all four themes, with students supported by a multi-disciplinary supervisory team. The CDT also connects to a wide range of external collaborators and partners, including AWE, BAE Systems, CERN, NPL, Max Planck Institute, Oxford University, QinetiQ.

Projects​

1. Biological and molecular systems

One focus of this theme is frontier research on matter that is derived from living things or that is still living. Living matter is distinct from inert condensed matter by being active, capable of self-assembly and response to stimuli. It is often composed of molecules of biological origin, such as nucleic acids.

PhD students in this theme will draw upon knowledge in soft matter physics, analytical chemistry and engineering to study the fundamental principles of how living matter self-organises. Objectives include controlling and engineering living matter to achieve functions such smart drug delivery via controlled response to external stimuli, and understanding the soft matter physics of virus survival in the environment.

In addition to this, the students will develop new ways to observe cellular materials at unprecedented levels of detail.  The centre will prepare scientists for careers in this rapidly growing interdisciplinary area, an area revolutionising our understanding of how living organisms from viruses to us function, as well as allowing unprecedented control over matter. Further information is available in the biological and molecular systems document (PDF).

1.1 Quantification of radiation-induced DNA damages using DNA nanotechnology: from single DNA to DNA clusters (W. Bae).

1.2 Studying noise-induced gene transitions using DNA computing (A. Rocco).

1.3 A Synthetic Biology Approach to Studying the Survival of Airborne Viruses such as SARS and Influenza (R. Sear).

1.4 Development of spatial single cell proteomics and application to probe bystander effects from proton beam irradiation in single cells (G. Grime).

1.5 Development of ZenoToF mass spectrometry for single cell “omics” (Sneha Pinto).

1.6 Derivatisation at the one cell level to enhance analyte coverage (M Bailey).

2. Computational methods and modelling

The importance of modelling and simulation in advancing new solutions to healthcare challenges is now widely recognised. Recent developments in systems biology and network science allow us to envisage a future where digital twins reach their potential to suggest new treatments and interventions. Equally systems modelling and analysis are rapidly progressing the fields of disease diagnosis across applications. Progressing these fields will require advances in computational modelling, optimisation techniques, high performance computing, and an expanding repertoire of observational data across multiple scales and modalities.

Collectively the projects in this theme will produce novel techniques to integrate and fuse data across multiple scales, analyse the resulting models, and lead to increased precision medicine and treatment. Applications include cancer modelling, dementia research and heart disease. The theme focus will be on innovation through predictive equation-based modelling and simulation. Further information is available in the computational methods and modelling document (PDF).

2.1 To dissect how drug resistance develops through rewiring of gene regulatory networks, and how vulnerabilities in rewired networks can be targeted therapeutically (Sotiris Moschoyiannis).

2.2 Modelling cell mechanical feedbacks in organoid development to direct organoid design (Carina Dunlop).

2.3 Computational inference, modelling and simulation of gene regulatory networks during neural development (Roman Bauer).

2.4 Modelling the spatial heterogeneity of gene regulatory networks in cancer, to improve our understanding of how the heterogeneity in these networks leads to drug resistance (Spencer Thomas).

2.5 Detection of peripheral artery disease using machine learning (Philip Aston).

2.6 Optimising shock wave therapy using modelling investigations of high-amplitude pressure waves on cells (Serge Cirovic).

3. Patient focussed technologies

This research theme develops emerging topics bioelectronic engineering, which is the application of electrical engineering, physical and chemical principles to biology, medicine, behaviour or health, to create a better digitally connected society for its health and wellbeing.

It uses electrical and photonic engineering approaches to integrated microsystems, with chemistry and physics leading to new biological and medical sensors and systems that interact and exchange data with the nervous system. These sensors consist of state-of-the-art ultrasonic, RF, optical, MRI, CT, Xray, electrical impedance transducers and are used to develop wearable and implantable devices, with decreasing size, weight, and power requirements and increased functionality. Further information is available in the patient focussed technologies document (PDF).

3.1 Flexible, printed sensor electronics for accurate disease detection and patient monitoring (Maxim Shkunov).

3.2 Energy harvesting and self-powering for medical implanted devices (Ravi Silva).

3.3 Thin-film mesh electronics system for chronic wound monitoring and therapy (Yunlong Zhao).

3.4 Development of novel tools applied to sound stimulation for the modulation of brain rhythms affected by ageing and disease (Daniel Abasolo).

3.5 Early and rapid detection of chemotherapy-induced nerve disorders (Matthew Oldfield).

3.6 Sensor fusion for health diagnostics in respiratory disorders (David Birch).

4. Sensors and imaging

The increasing demand for low-cost, precise, and accurate radiology analysis enabling early diagnosis and classification of disorders, alongside the sustained shortage of radiologists, is driving the demand for development and deployment of complex analysis solutions In this research theme we will develop and apply machine learning methods for clinically-focussed health and medical technologies.

High-quality and independent datasets will be used to clinically validate analysis methods for healthcare, along with robust validation procedures and reliable assessment frameworks. The research theme will also develop new sensor and measurement technologies, such as high sensitivity X-ray sensors and spectroscopic imaging systems, which will be applied to medical imaging applications. Further information is available in the sensors and imaging document (PDF).

4.1 In-line extraction of spectral features from hyperspectral X-ray and gamma images for quantitative analysis (Silvia Pani).

4.2 Novel approaches to scatter correction, reconstruction and dosimetry in therapeutic nuclear medicine (James Scuffham).

4.3 Perovskite high sensitivity X-ray and gamma detectors for medical imaging applications (Paul Sellin).

4.4 The chemistry of high efficiency perovskite nanoparticle X-ray detectors (Carol Crean)

4.5 Development of a genetic algorithm optimised fuzzy logic scintillator selection tool for medical physics (Caroline Shenton-Taylor)

Open to any UK or international candidates. Up to 30% of the University of Surrey funded studentships may be awarded to international students. Find out more about eligibility.