Funding

Self-funded

Project code

SEM10420526

Start dates

October, February and April

Application deadline

Applications accepted all year round

Applications are invited for a self-funded, 3 year full-time or 6 year part-time PhD project.

The PhD will be based in  the School of Electrical and Mechanical Engineering and supervised by Dr Mahsa Mehrad.

The work on this project will involve:

  • Designing and simulating advanced AlGaN-based power devices using TCAD tools to achieve high efficiency and high breakdown performance for EV applications.
  • Performing advanced electrical characterization of AlGaN prototype devices using state-of-the-art measurement techniques.
  • Applying AI and machine learning methods to accelerate device optimization, predict performance, and analyze reliability trends.
  • Evaluating device behaviour under realistic EV operating conditions (high voltage, high temperature, fast switching).
  • Collaborating with semiconductor and automotive industry partners, gaining experience in translating research into practical power electronics solutions.

 

Electric vehicles (EVs) are placing unprecedented demands on power electronic systems, requiring devices that can switch faster, operate at higher voltages, withstand higher temperatures, and maintain long-term reliability. To meet these challenges, wide-bandgap semiconductor materials—especially Aluminum Gallium Nitride (AlGaN)—are emerging as a key technology. This project investigates the design, modelling, characterisation, and AI-assisted optimisation of AlGaN high-electron-mobility transistors (HEMTs) for next-generation EV power electronics. The research integrates advanced simulation, innovative characterisation methods, and data-driven performance prediction to accelerate the development of efficient, compact, and robust semiconductor devices. 

The increasing global adoption of electric vehicles is driving the need for more efficient, compact, and reliable power electronic systems. Power devices are central to EV performance, influencing drivetrain efficiency, charging speed, thermal management, and battery lifetime. As conventional silicon devices approach their limits, wide-bandgap semiconductors offer a transformative opportunity. Among these materials, Aluminium Gallium Nitride (AlGaN) is particularly promising due to its high breakdown field, low on-resistance, and inherent robustness in demanding operating environments.

This PhD project aims to advance the design and understanding of AlGaN high-electron-mobility transistors (HEMTs) tailored specifically for EV applications. The research will combine detailed numerical simulation, precision electrical characterisation, and artificial intelligence (AI) methodologies to accelerate device optimisation. Simulation activities will involve the use of TCAD tools to analyse device physics, explore design variations, and evaluate performance trade-offs under realistic automotive conditions, including high voltage, high temperature, and fast-switching regimes.

Complementing the simulation work, the project will employ advanced electrical measurement techniques to characterise prototype AlGaN devices. Measurements may include static and dynamic I–V characterisation, capacitance evaluation, switching performance analysis, and reliability testing. These experiments will generate high-quality datasets essential for validating simulation models and informing device improvement strategies.

A key component of the project involves integrating AI and machine learning tools to enhance predictive modelling and design optimisation. By learning from simulation and measurement data, AI models will be developed to predict device behaviour, identify critical performance determinants, and accelerate the exploration of design space. This data-driven approach aims to reduce development cycles and uncover design solutions that might not emerge through conventional engineering methods.

The outcomes of this project will contribute to the broader understanding of AlGaN device physics and reliability, while providing practical pathways for improving EV power electronics. The research has the potential for significant real-world impact, supporting cleaner transportation technologies and advancing the capabilities of wide-bandgap semiconductors.

The student will be embedded within a supportive research environment, gaining expertise in semiconductor device simulation, electrical characterisation, data analytics, and AI-based modelling. Collaboration with academic and industrial partners may further enrich the research experience and broaden future career opportunities.

 

Fees and funding

Visit the research subject area page for fees and funding information for this project.

Funding availability: Self-funded PhD students only. 

PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only).

 

Bench fees

Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.

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 electrical engineering, Electronic Engineering, Semiconductor Devices, Materials Science, Physics, or a related area. 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.

 

  • A strong academic background in the related field.
  • Knowledge of semiconductor device physics, especially wide-bandgap or III-nitride materials (e.g., GaN, AlGaN).
  • Experience with numerical simulation tools is highly desirable.
  • Familiarity with electrical characterisation techniques is an advantage.
  • Interest or experience in machine learning / AI methods for modelling, optimisation, or data analysis.
  • Strong analytical skills and the ability to interpret complex physical phenomena.
  • Ability to work independently and as part of a multidisciplinary research team.
  • Good written and spoken communication skills for writing reports, publishing research findings, and presenting at conferences.

 

How to apply

We’d encourage you to contact Dr Mahsa Mehrad (mahsa.mehrad@port.ac.uk) to discuss your interest before you apply, quoting the project code.

 

When you are ready to apply, please follow the 'Apply now' link on the Electronic Engineering PhD subject area page and select the link for the relevant intake. 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 self-funded PhD opportunity you must quote project code SEM10420526 when applying.