Funding

Self-funded

Project code

CMP10281026

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 Computing and will be supervised by Dr Hamidreza Khaleghzadeh, Dr Alexander Gegov and Dr Alexey Lastovetsky.

 

The work on this project will:

  • Analyse performance and energy consumption of Machine Learning (ML) and Deep Learning (DL) algorithms on various computing platforms, including accelerators (e.g. GPUs) and resource-constrained devices (e.g., IoT hardware).
  • Develop analytical and AI-based models to predict performance and energy consumption of ML/DL workloads on different computing platforms. 
  • Establish theoretical foundations and design novel optimisation algorithms to improve the efficiency of ML/DL methods for computational performance and energy (electricity) consumption.

Machine Learning (ML) and Deep Learning (DL) algorithms are increasingly embedded in a wide range of applications, from autonomous systems and smart healthcare to financial modelling, environmental monitoring, and intelligent manufacturing. As these algorithms grow in complexity and scale, they demand substantial computational resources. This has led to growing concerns about their performance efficiency and energy consumption, particularly when deployed on heterogeneous platforms such as GPUs, edge devices, and Internet of Things (IoT) systems.

With ICT projected to consume 20% of global electricity by 2030, and data centre emissions rising sharply, improving the performance and energy efficiency of computing systems has become a critical challenge. The UK’s commitment to net-zero emissions by 2050 further highlights the need for sustainable and efficient digital technologies.

This PhD project addresses these challenges by focusing on the performance and energy optimisation of ML/DL algorithms across diverse computing systems. This research will investigate both hard-level and software-level optimisation strategies—specifically, optimising the algorithms themselves.

The project aims to develop analytical and AI-based models to understand and predict the performance and energy behaviour of ML/DL workloads across a range of platforms, including GPUs and IoT devices. Building on these models, the candidate will design and implement novel methods, algorithms, and software tools that enhance both computational performance and energy efficiency. A central aspect of the research will be the algorithmic redesign of existing ML/DL methods to better utilise heterogeneous computing architectures, enabling resource-aware, performance- and energy-optimised execution.

The supervisory team consists of Dr Hamidreza Khaleghzadeh, an expert in energy-efficient computing; Alexander Gegov, with over 15 years of research experience in machine learning applications in various fields; and , the director of the Heterogeneous Computing Lab (HCL) at University College Dublin, Ireland, with over 20 successful PhD completions.

The successful candidate will also have opportunities to visit and work with research staff in HCL, providing excellent opportunities for skills and career development.

 

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  (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 Computing, Computer Engineering, Electrical Engineering 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.

 

  • Good programming skills, especially in C/C++ and Python
  • Good knowledge of machine learning basics and experience with Data Analysis, Data Visualisation tools (Keras, TensorFlow, Pandas) and implementing various machine learning models
  • Excellent communication and academic writing skills
  • Fluency in working on Linux systems will be an advantage
  • Working experience with parallel computing concepts and familiarity with multithreaded and distributed tools and libraries, such as CUDA, OpenCL, MPI, will be an advantage

 

 

 

How to apply

We’d encourage you to contact Dr Hamidreza Khaleghzadeh  (hamidreza.khaleghzadeh@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 Computing 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. 

When applying please quote project code: CMP10281026