Funding

Self-funded

Project code

SEM10550529

Department

School of Electrical and Mechanical Engineering

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 will be supervised by Dr Salem Aljareh and Prof. Bill Dawber (QinetiQ). 

 

 

 

The work on this project will:

  • Investigate how human motion uniquely affects wireless signals (Wi-Fi, radar, UWB)
  • Develop privacy-preserving human signature characterisation methods
  • Analyse temporal and spectral RF features such as micro-Doppler
  • Evaluate robustness across environments and user condition

 

This PhD study is part of a collaboration between the university and an industrial partner (leading company in technology and engineering) and the candidate will benefit from the collaboration during and after the study.

This project investigates whether individuals can be characterised using passive wireless sensing without relying on cameras or wearable devices. Human movement and physical characteristics introduce subtle variations in radio signals, which can be captured through technologies such as Wi-Fi Channel State Information (CSI), mmWave radar, and Ultra-wide band (UWB).

The research will focus on extracting and analysing features such as micro-Doppler signatures, temporal variations, and multipath patterns to understand their stability and distinctiveness. The student will design experimental setups, collect multi-session datasets, and develop signal processing techniques to isolate human-induced effects from environmental noise.

Emphasis will be placed on privacy-preserving approaches, avoiding explicit biometric identification while enabling reliable characterisation of human motion patterns. The project will also evaluate performance under realistic conditions, including environmental changes. Outcomes will include new feature representations and lightweight methods suitable for real-world deployment.

 

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 - eligibility criteria apply).

 

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 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.

International students will require a study visa from UKVI to pursue the degree in the UK. If the research is in a sensitive or technological subject, the student may also need to secure an Academic Technology Approval Scheme (ATAS) certificate from the UK Foreign Office.

 

You’ll have knowledge in radio signals, DSP (Digital Signal Processing), machine learning, data analytics. You should also demonstrate strong programming in MATLAB/python/java and mathematical analysis skills.

Experience in C/C++ programming is desirable. 

 

How to apply

We’d encourage you to contact Dr Salem Aljareh ([email protected]) 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 SEM10550529 when applying.