Funding

Self-funded

Project code

CMP10041025

Department

School of Computing

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 Mohamed Bader and Dr Alexey Lastovetsky.

The work on this project will:

  • Develop theoretical foundations and algorithms that enable designing energy-efficient applications with a low energy footprint.
  • Analyse performance and energy consumption on various computing platforms, including accelerators (GPUs) and IoT devices.
  • Create predictive models using machine and deep learning methods to estimate component-level performance and energy consumption in computing platforms.

The share of ICT in electricity consumption is quickly increasing and is set to reach 20% of all electricity demand by 2030. Current trends indicate that by 2040, global carbon emissions from centres will be 40% of transportation’s current level. In 2018, the UK Government established a goal to achieve carbon neutrality, or net zero, by 2050. One key aspect of achieving NetZero is to improve the sustainability and energy efficiency of applications and computing platforms. 

 

The School of Computing at the 1024ºË¹¤³§ seeks a qualified PhD candidate to research energy-efficient computing. This project aims to develop models, methods, algorithms, and software to optimise the energy and performance of applications on contemporary computing platforms, including the Internet of Things and various computing systems, such as accelerators (GPUs). While mainstream approaches to energy-efficient computing focus on optimising the execution environment, this project will concentrate on optimising the applications themselves. This approach holds significant potential for energy savings but has been understudied due to the complexity of the associated engineering and scientific challenges.

 

In this project, we will leverage data analytics and machine/deep learning tools to produce accurate energy and performance measurement and modelling approaches. The results of the proposed measurement techniques will be used to develop, train, and refine a sustainable computing framework, identifying all energy-efficient configurations of applications. The developed methods and algorithms will apply to a wide range of applications, thereby enhancing sustainability across general computing platforms.

 

The supervisory team consists of Dr Hamidreza Khaleghzadeh, an expert in energy-efficient computing, Dr Mohamed Bader-El-Den, 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 the HCL group, 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 computer science 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

Good knowledge of machine learning basis 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: CMP10041025