Research hub considers response to life beyond Earth

The new SETI Post-Detection Hub, coordinated by Dr John Elliott, an honorary research fellow of the School of Computer Science has gathered widespread press interest, across UK, Europe, USA, Canada, South America and South East Asia.

The Hub hosted by the Centre of Exoplanet Science and the Centre for Global Law and Governance of the University of St Andrews, will act as a coordinating centre for an international effort bringing together diverse expertise across both the sciences and the humanities for setting out impact assessments, protocols, procedures, and treaties designed to enable a responsible response should we discover intelligent life forms beyond our planet.

More information about the hub can be found in the university press release

Phd Scholarships for 2023

Scholarship Description
The School of Computer Science is offering the following types of scholarships for 3.5 years of study in our PhD programme. UK, EU and International students are all eligible for:

• Fully funded scholarships consisting of tuition + stipend
• Tuition-only scholarships

This award is part-funded through the University’s new ‘handsels’ scheme.

Value of Award
• Tuition scholarships cover PhD fees irrespective of country of origin.
• Stipends are valued £17,668 per annum (or the standard UKRI stipend, if it is higher).

Eligibility Criteria
We are looking for highly motivated research students willing to be part of a diverse and supportive research community. Applicants must hold a BSc or MSc in Computer Science or a related area appropriate for their proposed topic of study.

International applications are welcome. We especially encourage female applicants and underrepresented minorities to apply.

Application Deadline
All applications received before 1st February 2023 will be considered for the first round of scholarship eligibility. Later applications will also be considered for scholarships as long as funding remains.

How to Apply

If accepted, every PhD application indicating interest will automatically be considered for these scholarships. There is no need for a separate application.

The best way to win one of our scholarships is to make a robust PhD application. You are strongly encouraged to approach supervisors before formal submission to discuss your project ideas with them.

The School’s main groups are Artificial Intelligence and Symbolic Computation, Computer Systems and Networks, Human-Computer Interaction, and Programming Languages. It is highly recommended that applicants identify potential supervisors in their applications. A list of existing faculty and areas of research can be found at All supervisors listed on this page may be contacted directly to discuss possible projects.

Full application instructions can be found at
Inquiries and questions may be directed to

PhD Viva Success: Chawanangwa Lupafya

Please join me in congratulating Chawanangwa Lupafya, who has just passed his PhD viva subject to minor corrections.

Chawanangwa is supervised by Dr Dharini Balasubramaniam.

Special thanks to Dr Ruth Hoffman for serving as internal examiner and  Dr Rami Bahsoon from the University of Birmingham for serving as the external examiner


PhD Viva Success: Yasir Alguwaifli

Please join me in congratulating Yasir Alguwaifli, who has just passed his PhD viva subject to minor corrections.

Yasir, who is supervised by Christopher Brown, has provided his thesis abstract below.

Thanks to Özgür Akgün for serving as internal examiner and Prof Christoph Kessler from Linköping University for serving as the external examiner.

Controlling energy consumption has always been a necessity in many computing contexts as the resources that provide said energy is limited, be it a battery supplying power to an Single Board Computer (SBC)/System-on-a-Chip (SoC), an embedded system, a drone, a phone, or another low/limited energy device, or a large cluster of machines that process extensive computations requiring multiple resources, such as a Non-Uniform Memory Access (NUMA) system. The need to accurately predict the energy consumption of such devices is crucial in many fields. Furthermore, different types of languages, e.g. Haskell and C/C++, exhibit different behavioural properties, such as strict vs. lazy evaluation, garbage collection vs. manual memory management, and different parallel runtime behaviours. In addition most software developers do not write software with energy consumption as a goal, this is mostly due to the lack of generalised tooling to help them optimise and predict energy consumption of their software. Therefore, the need to predict energy consumption in a generalised way for different types of languages that do not rely on specific program properties is needed. We construct several statistical models based on parallel benchmarks using regression modelling such as Non-negative Least Squares (NNLS), Random Forests, and Lasso and Elastic-Net Regularized Generalized Linear Models (GLMNET) from two different programming paradigms, namely Haskell and C/C++. Furthermore, the assessment of the statistical models is made over a complete set of benchmarks that behave similarly in both Haskell and C/C++. In addition to assessing the statistical models, we develop meta-heuristic algorithms to predict the energy consumed in parallel benchmarks from Haskell’s Nofib and C/C++’s Princeton Application Repository for Shared-Memory Computers (PARSEC) suites for a range of implementations in PThreads, OpenMP and Intel’s Threading Building Blocks (TBB). The results show that benchmarks with high scalability and performance in parallel execution can have their energy consumption predicted and even optimised by selecting the best configuration for the desired results. We also observe that even in degraded performance benchmarks, high core count execution can still be predicted to the nearest configuration to produce the lowest energy sample. Additionally, the meta-heuristic technique can be employed using a language- and architecture-agnostic approach to energy consumption prediction rather than requiring hand-tuned models for specific architectures and/or benchmarks. Although meta-heuristic sampling provided acceptable levels of accuracy, the combination of the statistical model with the meta-heuristic algorithms proved to be challenging to optimise. Except for low to medium accuracy levels for the Genetic algorithm, combining meta-heuristics demonstrated limited to poor accuracy.

Research participants from further education wanted

We are looking to speak to further education students of all disciplines.

Photo by David Kennedy on Unsplash

We want to understand what students want to know about personal cyber security and how they want to learn it.   To participate, you must be 16 or over and based at college, not at school, and willing to take part in an interview about this.  Sessions will last a maximum of 30-40 minutes, held on Microsoft Teams. Participants will be offered a £8 voucher for their time and contributions.

If you are interested, please get in contact using the details below. You will then be given a Participant Information Sheet with further details of our research and have the opportunity to ask questions, before being asked whether you consent to participate.

Contact Details

Amy Hunt  –

This study is being conducted as part of a research study in the School of Computer Science at the University of St Andrews.  The researchers are Dr Jean Carletta, Kevin Doherty, Amy Hunt, and Molly Wilson.


Learning to Describe: A New Approach to Computer Vision Based Ancient Coin Analysis

The work on deep learning based understanding of ancient coins by Jessica Cooper, who is a Research Assistant and a part-time PhD student supervised by Oggie Arandjelovic and David Harrison has been chosen as a featured, “title story” article by the Journal Sci where it was published in a Special Issue Machine Learning and Vision for Cultural Heritage.

Leverhulme Early Career Fellowship for Nguyen Dang

Congratulations to Dr Nguyen Dang, who has been awarded a Leverhulme Trust Early Career Fellowship. The 3 year Fellowships are intended to assist those at an early stage of their academic careers to undertake a significant piece of publishable work. Nguyen will be researching Constraint-based automated generation of synthetic benchmark instances.

Abstract summary: “Combinatorial problems such as routing or timetabling are ubiquitous in society, industry, and academia. In the quest to develop algorithms to solve these problems effectively, we need benchmark instances. An instance is an example of the problems at hand for testing how well an algorithm performs. Having rich benchmarks of instances is essential for algorithm developers to gain understanding about the strengths and weaknesses of their approaches, and ensure successful applications in practice. This fellowship will provide a fully automated system for generating valid and useful synthetic benchmark instances based on a constraint modelling pipeline that supports several algorithmic techniques.”