Project code



School of Energy and Electronic Engineering

Start dates

October 2021

Closing date

4 May 2021

This project is now closed. The details below are for information purposes only.
View our current projects here.

Applications are invited for a fully-funded three year PhD to commence in October 2021. 

The PhD will be based in the School of Energy and Electronic Engineering  and will be supervised by Dr Mojtaba Ghodsi

Candidates applying for this project may be eligible to compete for one of a small number of bursaries available; these cover tuition fees at the UK rate for three years and a stipend in line with the UKRI rate (£15,609 for 2021/22). Bursary recipients will also receive a £1,500 p.a. for project costs/consumables. 

The work on this project could involve:

  • Nonlinear vibration analysis, numerical simulation, experimental verification  
  • Manufacturing, interfacing and data acquisition
  • Coding (i.e. MATLAB/Simulink)

University of Portsmouth (UoP) is placed among the top 100 “Universities under 50” in the world. To mitigate its carbon footprint, UoP recently shows its interest to install energy-efficient LED lighting and daylight sensors in the buildings.  

Nowadays, the Internet of Things (IoT) is employed to enhance the performance of the energy sector. Almost all sections of the energy sector including generation, transmission, distribution, and demand use a large number of wireless sensors and communication technologies. The new generation of MEMS sensors, low-power VLSI, and CMOS is characterised by extremely low power consumption. Although high energy density lithium-ion batteries can now be employed in the majority of electrical devices, in some applications, their replacement can be a big challenge. For example, the replacement of a battery of proximity or humidity sensor in the nacelle of thousands of offshore wind turbine generators is a very costly and time-consuming process. To tackle the difficulties of replacement/ recharging of the batteries, the extremely low power wireless sensors can be energized by converting the available environmental energy to electricity through energy harvesters. This conversion can be from solar light, vibration, or heat to electricity. Therefore, high-quality energy harvesters play a significant role to provide self-powered monitoring system.   

The aim of this PhD research project is the development of a self-powered monitoring system that is suitable for the energy sector. The researcher will concentrate on developing of vibration-based energy harvester to energize the low power sensors (such as temperature, humidity and light among others).  Control of energy consumption of a building (i.e. lighting, heating, cooling, and air conditioning) by the developed self-powered monitoring system will be utilised as the case study for this project.  Applicants should have an interest in harvester and low-power sensors as well as detailed knowledge of electronics, manufacturing, and smart materials (for example, magnetostrictive).

Entry requirements

You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in an appropriate subject. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

You should have good knowledge of mechatronics, nonlinear vibration analysis, energy harvesting and low-power sensing electronics.

How to apply

We’d encourage you to contact Dr Mojtaba Ghodsi ( to discuss your interest before you apply, quoting the project code.

When you are ready to apply, you can use our online application form. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV.  Our ‘How to Apply’ page offers further guidance on the PhD application process. 

If you want to be considered for this funded PhD opportunity you must quote project code SENE5950521 when applying.