Funding

Competition funded (UK/EU and international students)

Project code

SENE8720124

Department

School of Energy and Electronic Engineering

Start dates

April 2024

Application deadline

19 January 2024

Applications are invited for a fully-funded three year PhD to commence in April 2024. 

The PhD will be based in the Schools of Energy and Electronic Engineering and Mechanical and Design Engineering, and will be supervised by Dr Hongjie Ma, Dr Edward Smart and Prof. Victor Beccera. 


Successful applicants will receive a bursary to cover tuition fees for three years and a stipend in line with the UKRI rate (£18,622 for 2023/24). Bursary recipients will also receive a £1,500 p.a. for project costs/consumables.

 

The work on this project could involve:

  • Algorithm selection and optimisation: Choose algorithms suitable for low-power, limited computation scenarios, such as decision trees, support vector machines, or lightweight deep learning algorithms like MobileNet or LSTM.
  • Model compression and pruning: Reduce model complexity by applying compression techniques (e.g., quantisation, weight sharing) and pruning methods to remove redundant parameters, lowering computation and memory requirements.
  • Computation resource scheduling: Balance computational accuracy and speed through dynamic adjustment, selecting different algorithms or models based on task demands and hardware resources.
  • System optimisation: Enhance code execution efficiency by using MCU/DSP-specific compilers, runtime libraries, and processor-specific code optimisations, including assembly instructions and hardware acceleration features.
  • Low-power design: Implement low-power strategies, such as dynamic voltage frequency scaling (DVFS) and sleep/wake control, while maximising processor-built low-power features and peripherals, like low-power clock modes and disabling unnecessary devices.

 

Are you a passionate and talented UK/EU student eager to contribute to cutting-edge research in artificial intelligence (AI) and embedded systems? We are offering a fully funded PhD position to explore the development and deployment of AI algorithms on embedded systems with limited computational capacity and power consumption, with the aim of expanding their applications in industrial settings and daily life. You will be supervised by an experienced team led by Dr Hongjie Ma, with expertise in embedded system development and AI, Dr Edward Smart, a renowned expert in AI and industry-academia collaboration, and Prof Victor Becerra, a distinguished leader in energy and control systems with an extensive background in AI and rich PhD supervision experience.

This project presents exceptional opportunities for both academic and industrial collaboration. On the academic front, your research could facilitate the integration of AI into ongoing research projects involving embedded systems at our institution, such as audio signal processing, ultrasonic signal processing, and low-power consumption. In terms of industrial collaboration, our supervisory team is actively involved in projects with industrial companies, which all implement AI on embedded systems. Your PhD project will help strengthen existing partnerships and create new project opportunities. Your work will focus on algorithm selection and optimisation, model compression and pruning, computation resource scheduling etc.

Join us in pushing the boundaries of AI and embedded systems and become part of our thriving research community. Apply now for this exciting and fully funded PhD opportunity.

 

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.

Foundational knowledge in artificial intelligence, particularly in algorithm selection and optimisation for resource-constrained environments. Familiarity with decision trees, support vector machines, and lightweight deep learning algorithms, such as MobileNet or LSTM, is essential.

Expertise in embedded system optimisation, particularly with respect to code execution efficiency on embedded systems. Experience with MCU/DSP-specific compilers, runtime libraries, and processor-specific code optimisations (including assembly instructions and hardware acceleration features) is required.

In addition to the technical skills outlined above, candidates should possess excellent analytical and problem-solving abilities, strong written and verbal communication skills, and a collaborative mindset. A bachelor's or master's degree in computer science, electrical engineering, or a closely related field is required.

 

 

 

How to apply

We’d encourage you to contact Dr Hongjie Ma (Hongjie.ma@port.ac.uk) 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 SENE8720124 when applying.