Problem Statement

The need for reliable and efficient healthcare is ever-growing in our societies. Global pandemics such as COVID-19, chronic diseases such as diabetes, and cancer treatments require efficient testing capabilities for large number of patients. Keeping healthcare cost under control whilst providing excellent services is one of the major challenges faced by the modern world. This is especially so for many developed countries that are now facing a surge in ageing population and reduced birth rates. Recent advances in material sciences and artificial intelligence have shown the potentials for a new generation of reliable, accurate, and cost-efficient biomedical sensing devices that can diagnose and cure various types of diseases.

Apart from the technological advancements in biomedical engineering to improve test quality, the efficiency in mass production of such sensors is critical as well. Traditional manufacturing methods of the sensing platform have several drawbacks such as complicated fabrication steps, high fabrication costs and time consumptions, as well as low manufacturing scalability and flexibility. Chronic diseases and viruses affecting millions of people worldwide have driven the need for the availability of easy-to-use and cost-efficient testing devices for immediate diagnosis and treatment.

Proposed Innovative Solution

This project focuses on the industrialisation of manufacturing processes for biomedical sensing devices. In the first phase of the project, a detailed analysis of current manufacturing processes for biomedical sensor technologies will be conducted. This also requires an analysis of the material properties and related manufacturing technologies for commercial production of new sensor devices. Additionally, a modelling of pivotal production steps and their potential for automation have to be investigated. In the second phase, new manufacturing processes will be developed using results from other prominent industrial branches such as 3D printing and their potential for mass production as well as the optimisation of existing processes with a special focus on cost efficiency. Tools like process and multi-physics simulations to model, analyse, and customise processes will be used to develop concepts for the required industrialised facilities.

A combination of model-based control techniques and data-driven methods can be used to complement this study. Patient-specific customisation of test devices will be critical in the future, hence methods for complexity management of highly varying sensor devices with enhanced robustness will be studied. Finally, the potential for automation will be studied. Industrial automation should be used only if efficiency savings can be realised. This relates to material and process logistics using, e.g. automated guided vehicles, process integrated quality control measures to improve overall product quality as well as optimisation of individual production processes and technologies for increased productivity. As a result, the project should yield a concept for cost-efficient mass production of biomedical sensing devices — example of such concept includes but not limited to: how to efficiently set up production facilities for new biomedical sensor devices, such as those used to provide reliable and low-cost measurements of ECGs.

Essential Criteria:

1)At least a 2:1 Honours degree (or equivalent) in Electrical and Electronic Engineering, Biomedical Engineering, Computer Science, Industrial Engineering, Applied Mathematics or a related subject.

2)A relevant Master’s degree and/or experience in one or more of the following will be an advantage: modelling of biomedical sensors, industrial engineering, deep learning and machine learning, material science, digital signal processing, MATLAB, Simulink, PLC

3)Strong understanding of the mathematical sciences and its applications to Engineering.

4)Excellent written and spoken communication skills in English.

This project is part of the Advanced Biomedical Engineering Laboratory (BoDevices Lab) at Ulster University, which is a £7 million initiative for strategic partnership between Invest Northern Ireland (Invest NI), Ulster University, Randox Laboratories, and Heartsine Technologies. The Invest NI’s R&D support is part funded by ERDF under the EU Investment for Growth and Jobs Programme 2014–2020.

Essential criteria

  • To hold, or expect to achieve by 15 August, an Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC) in a related or cognate field.

This project is funded by: Invest NI

The scholarship will cover tuition fees at the Home and EU rate and a maintenance allowance of £ 15,285 per annum for three years.

The Doctoral College at Ulster University