Vacancies & Opportunities

PhD Projects

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.

This PhD project aims at developing an integrated multifunctional device for gesture recognition applications. As part of the project, the range of current wearable devices, sensor architectures, and material properties will be surveyed. Based on data-driven approaches, an effective multiphysics model will be created to study the behaviour of soft electromechanical sensors made of different structures and materials. Machine learning tools will be utilised to optimise the performance of soft sensors as well as the power consumption of the entire integrated device. The project involves fabrication of soft sensors through digital manufacturing technologies including 3D printing and laser micromachining. The candidate will conduct various electromechanical tests and materials characterisation to evaluate the performance of the manufactured sensors. Furthermore, different strategies will be examined for the robust integration of soft sensors with other electronic components. The experimental data will be used to validate the proposed model. This is an exciting opportunity to work on a dynamic and interdisciplinary research topic that intertwines engineering, materials science, computer science, and healthcare technology. For further information, please get in touch with Dr Morteza Amjadi and/or Dr Rami Ghannam.


1- M. Amjadi, K.-U. Kyung, I. Park, and M. Sitti “Stretchable, Skin-mountable, and Wearable Strain Sensors and Their Potential Applications: A Review”, Advanced Functional Materials, 2016, 26, 1678.

2- Y. Jeong, J. Gu, J. Byeon, J. Ahn, J. Byeon, K. Kim, J. Park, J. Ko, J. Jeong, M. Amjadi, and I. Park “Ultra-Wide Range Pressure Sensor based on Microstructured Conductive Nanocomposite for Wearable Workout Monitoring”, Advanced Healthcare Materials, 2021, 10, 2001461.

3- Liu, Y., Liang, X., Li, H., Deng, H., Zhang, X., Wen, D., Yuan, M., Heidari, H., Ghannam, R. and Zhang, X. (2022) Ultralight smart patch with reduced sensing array based on reduced graphene oxide for hand gesture recognition. Advanced Intelligent Systems, 4(11), 2200193. (doi: 10.1002/aisy.202200193)

4- Tanwear, A., Liang, X., Paz, E., Böhnert, T., Ghannam, R. , Ferreira, R. and Heidari, H. (2022) Spintronic eyeblink gesture sensor with wearable interface system. IEEE Transactions on Biomedical Circuits and Systems, 16(5), pp. 779 792. (doi: 10.1109/TBCAS.2022.3190689) (PMID:35830413)

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.


1- Hatem, T., Ismail, Z., Elmahgary, M. G., Ghannam, R. , Ahmed, M. A. and Abdellatif, S. O. (2021) Optimization of organic meso-superstructured solar cells for underwater IoT² self-powered sensors. IEEE Transactions on Electron Devices, 68(10), pp. 5319-5321. (doi: 10.1109/TED.2021.3101780)

2- Hatem, T., Elmahgary, M. G., Ghannam, R. , Ahmed, M. A. and Abdellatif, S. O. (2021) Boosting dye-sensitized solar cell efficiency using AgVO3-doped TiO2 active layer. Journal of Materials Science: Materials in Electronics, 32, pp. 25318-25326. (doi: 10.1007/s10854-021-06990-4)

3- Zhao, J., Ghannam, R. , Law, M. K., Imran, M. A. and Heidari, H. (2020) Photovoltaic power harvesting technologies in biomedical implantable devices considering the optimal location. IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, 4(2), pp. 148-155. (doi: 10.1109/JERM.2019.2937970)