Vacancies & Opportunities
We are always interested in recruiting PhD students (EU, UK and Self-funded) to join our team and work with us on a variety of design projects. Please get in touch if you are interested in any of the following projects:
A Data-Driven Approach to Designing High-Performance Soft Electromechanical Sensors for Human-Machine Interfaces
Extensive research is being conducted on the design and development of flexible electromechanical sensors (e.g., strain and pressure sensors) as they have significant potential in healthcare monitoring, human-robot interaction, and soft robotics. Compared to their traditional rigid counterparts, soft electromechanical sensors based on elastomers and nanocomposites offer high-quality readout together with comfort over prolonged use. Remarkable advances in materials science, nanotechnology, and bioinspired designs have significantly improved the performance of soft sensors. However, the development of novel soft sensors and their integration with other components (e.g., power source, data acquisition, communication, etc.) heavily relies on experiments and formulation of new materials and structures, which are expensive and time-consuming. Recently, data-driven approaches and machine learning have shown promising results in the integration and optimisation of soft sensors for practical use.
Machine learning for accelerating the discovery of HIGH-PERFORMANCE low-cost solar cells for wearable HMI Applications.
The development of advanced technologies such as wearable, implantable, and Internet of Things (IoT) devices in recent years has coincided with a growing interest in efficient energy harvesting solutions. Accordingly, scavenging energy from sunlight using photovoltaic (PV) cells can profoundly enhance the operation of these miniaturized electronic devices. Such devices primarily rely on rechargeable batteries to satisfy their energy needs. However, since PV technology is a mature and reliable method for converting the Sun’s vast energy into electricity, innovation in developing new materials and solar cell architectures are needed to ensure lightweight, portable, and flexible miniaturized electronic devices. Moreover, Artificial Intelligence (AI) and Machine Learning (ML) techniques are touted to be game changers in the area of energy harvesting. Thus, the aim of this PhD project is to investigate how ML algorithms can help improve the performance of low-cost PV cells for wearable HMI applications. Once developed, the aim is to integrate this design on a practical self-powered platform for gesture recognition. The candidate will also investigate the performance of this optimised system and make comparisons with state-of-the-art energy harvesters. For further information, please get in touch with Dr Rami Ghannam and/or Dr Morteza Amjadi.