School of Computer Science

Fully funded PhD scholarship in Algorithms for Data Science

Lead supervisor: Dr Peter Macgregor

Application deadline: 1 March 2025

Project description:

Modern data science and machine learning applications involve datasets with millions of data points and hundreds of dimensions. For example, deep learning pipelines produce massive vector datasets representing text, image, audio and other data types. The analysis of such datasets with classical algorithms often requires significant time and/or computational resources which may not be available in many applications.

This motivates the development of a new generation of fast algorithms for data analysis, running in linear or sub-linear time and often producing an approximate result rather than an exact one. Moreover, the dataset may change over time, requiring dynamic algorithms which handle updates efficiently.

This project will tackle aspects of the design, analysis, and implementation of algorithms for processing large dynamic datasets, with the aim to develop new algorithms with state-of-the-art practical performance and/or theoretical guarantees. This could involve performing new analysis of existing algorithms, designing new algorithms with provable guarantees, or implementing heuristic algorithms with state-of-the-art empirical performance.

Possible Directions

Potential areas of research, depending on the interests of the candidate include:

  • Developing improved nearest-neighbour search algorithms (e.g., based on kd-trees, HNSW, locality-sensitive hashing).
  • Exploring any connection between hierarchical clustering algorithms and nearest-neighbour search algorithms.
  • Creating new dynamic or hierarchical clustering algorithms (e.g. based on spectral clustering or DBSCAN).
  • Creating dynamic algorithms for numerical linear algebra. For example, maintaining the PCA of a dynamically changing dataset.
  • Any other project in the area of algorithmic data science and machine learning.

Applicants should have a strong interest in the mathematical analysis of algorithms, knowledge of topics in discrete mathematics and linear algebra, and some familiarity with existing algorithms for data analysis and machine learning. Strong programming skills would also be desirable.

The scholarship:

We have one fully-funded scholarship available, starting in September 2025. The scholarship covers all tuition fees irrespective of country of origin and includes a stipend valued at £19,705 per annum. More details of the scholarship can be found here: https://blogs.cs.st-andrews.ac.uk/csblog/2024/10/24/phd-studentships-available-for-2025-entry/, but please note the different application deadline.

Eligibility criteria:

We are looking for highly motivated research students keen to be part of a diverse and supportive research community. Applicants must hold a good Bachelor’s or Master’s degree in Computer Science, or a related area appropriate for the topic of this PhD.

International applications are welcome. We especially encourage female applicants and underrepresented minorities to apply. The School of Computer Science was awarded the Athena SWAN Silver award for its sustained progression in advancing equality and representation, and we welcome applications from those suitably qualified from all genders, all races, ethnicities and nationalities, LGBT+, all or no religion, all social class backgrounds, and all family structures to apply for our postgraduate research programmes.

To apply:

Interested applicants can contact Peter Macgregor with an outline proposal.

Full instructions for the formal application process

The deadline for applications is 1 March 2025.

PGR Seminar with Daniel Wyeth and Ferdia McKeogh

The next PGR seminar is taking place this Friday 15th November at 2PM in JC 1.33a

Below is a Title and Abstract for Daniel’s and Ferdia’s talks – Please do come along if you are able.

Daniel:

Deep Priors: Integrating Domain Knowledge into Deep Neural Networks

Deep neural networks represent the state of the art for learning complex functions purely from data.  There are however problems, such as medical imaging, where data is limited, and effective training of such networks is difficult.  Moreover, this requirement for large datasets represents a deficiency compared to human learning, which is able harness prior understanding to acquire new concepts with very few examples.  My work looks at methods for integrating domain knowledge into deep neural networks to guide training so that fewer examples are required.  In particular I explore probabilistic atlases and probabilistic graphical models as representations for this prior information, architectures which enable networks to use these, and the application of these to problems in medical image understanding.

Ferdia:

“Lessons Learned From Emulating Architectures”

Automatically generating fast emulators from formal architecture specifications avoids the error-prone and time-consuming effort of manually implementing an emulator. The key challenge is achieving high performance from correctness-focused specifications; extracting relevant functional semantics and performing aggressive optimisations. In this talk I will present my work thus far, and reflect on some of the unsuccessful paths of research.

Doughnuts will be available! 🍩

Fully funded PhD Scholarship in Hardware Simulation at Scale

 

As the Internet ofThings (IoT) expands, the number of connected devices is expected to reach close to 30 billion by 2030. These devices range from simple sensors to complex embedded systems, each with unique characteristics and communication protocols. Simulating such a vast and diverse array of devices presents a significant challenge in terms of scalability, accuracy, and efficiency. This PhD project aims to develop a comprehensive framework for simulating many (1000s, 10,000s, 1,000,000s) heterogeneous IoT devices, at (hopefully) close to real-time speeds. The project will focus on designing a specialised languages for describing hardware and simulations, creating an efficient simulation environment, and exploring hardware acceleration techniques to achieve high performance and scalability.

Previous research in this area has primarily focused on simulating individual devices, smaller networks, or using simplified models that do not fully capture the intricacies of real-world IoT systems. This project seeks to address these limitations by developing a scalable simulation framework that can accurately model the behaviour of billions of heterogeneous devices, advancing the state-of-the-art in simulation languages, distributed computing, and hardware acceleration.

The project will be structured around three core research ideas:

  • Simulation Languages for Heterogeneous Embedded Devices: The first research objective is to explore the creation of a specialised language for describing the behaviour and interactions of heterogeneous IoT devices. This language will need to be expressive enough to capture the wide range of device architectures and communication protocols found in IoT systems. The language will also support modularity and extensibility, allowing users to easily incorporate new device types and behaviours into the simulation.
  • Development of a Scalable Simulation Environment: The second research objective is to create a simulation environment that can efficiently emulate IoT devices at scale, across multiple simulation servers. This environment will be designed to support distributed computing, allowing for parallel execution of simulated devices across a large number of servers. The project will explore various techniques for load balancing, synchronisation, and communication between servers to ensure that the simulation remains efficient and accurate as the scale increases.
  • Hardware Acceleration for Large-Scale Simulations: The third research objective is to investigate the use of hardware acceleration techniques, such as Field Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs), to improve the performance of large-scale IoT simulations. This aspect of the project will focus on identifying the components of the simulation that can be offloaded to specialised hardware, and developing algorithms and architectures that leverage this hardware to achieve significant performance gains.

Topics of Interest

  • Heterogeneous Systems Modelling: Techniques for accurately modelling the diverse architectures and communication protocols of IoT devices.
  • Distributed Simulation: Methods for efficiently distributing simulations across multiple servers, including load balancing, synchronisation, and inter-server communication.
  • Simulation Languages: Design and implementation of specialised languages for describing complex IoT devices and networks.
  • Hardware Acceleration: Exploration of FPGA, GPU, and other hardware acceleration technologies to enhance the performance of large-scale simulations.
  • Scalability and Performance Optimisation: Strategies for ensuring that the simulation framework can handle the increasing complexity and scale of IoT networks.
  • Validation and Verification: Techniques for validating and verifying the accuracy and reliability of large-scale IoT simulations.

The Scholarship

We have one fully-funded scholarship available, starting in September 2025, which will be awarded to competitively to the best applicant. The scholarship covers all tuition fees (irrespective of country of origin) and comes with a stipend valued at £19,705 per annum. More details can be found here: https://blogs.cs.st-andrews.ac.uk/csblog/2024/10/24/phd-studentships-available-for-2025-entry/

International applications are welcome. We especially encourage female applicants and underrepresented minorities to apply. The School of Computer Science was awarded the Athena SWAN Silver award for its sustained progression in advancing equality and representation, and we welcome applications from those suitably qualified from all genders, all races, ethnicities and nationalities, LGBT+, all or no religion, all social class backgrounds, and all family structures to apply for our postgraduate research programmes.

To Apply

Informal enquiries can be directed to Tom Spink. Full instructions for formal applications can be found at https://www.st-andrews.ac.uk/computer-science/prospective/pgr/how-to-apply/

The deadline for applications is 1 March 2025.

PhD studentships available for 2025 entry

The School of Computer Science at the University of St Andrews is offering a number of PhD scholarships for 3.5 years of study in our doctoral research programme. UK, EU and International students are all eligible for fully-funded scholarships consisting of tuition and a stipend. These awards are part-funded through the University of St Andrews’ ‘handsel’ scheme for tuition waivers.

The School of Computer Science is a centre of excellence for computer science teaching and research, with staff and students from Scotland and all parts of the world. It is a member of the Scottish Informatics and Computer Science Alliance (SICSA).

Value of Award

  • Tuition scholarships cover PhD fees irrespective of country of origin.
  • Stipends are valued at £19,795 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 good Bachelor’s or Master’s degree 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. The School of Computer Science was awarded the Athena SWAN Silver award for its sustained progression in advancing equality and representation, and we welcome applications from those suitably qualified from all genders, all races, ethnicities and nationalities, LGBT+, all or no religion, all social class backgrounds, and all family structures to apply for our postgraduate research programmes.

Application Deadline

All applications received before 1st February 2025 will be considered for these scholarships.

How to Apply

Any PhD application received by the deadline will be automatically considered for these scholarships. There is no need for a separate application.

The School’s main research themes are Artificial Intelligence, Health Informatics, Human-Computer Interaction, Programming Languages, and Systems. You can find further details at https://www.st-andrews.ac.uk/computer-science/research/themes/. In addition, we have cross-cutting research groups in Complex and Adaptive Systems, Computer Vision, Constraints, Data Science, MedTech, Research Software, and Responsible Computing. Applicants with interests in any of these areas are encouraged to develop a relevant research proposal and apply.

The best way to obtain a place and a scholarship is to make a robust PhD application. You are strongly encouraged to read the application guidance written on our webpages. Note that this guidance asks you to approach supervisors before formal submission to discuss your project ideas with them. Historically, applications with no named supervisor have been much less likely to result in an offer. We provide a list of existing faculty, areas of research and some potential project ideas. All supervisors listed on this page may be contacted directly to discuss possible projects. You can define your own project or discuss a project currently on offer.

Full application instructions can be found at https://www.st-andrews.ac.uk/computer-science/prospective/pgr/how-to-apply/. Enquiries and questions may be directed to pg-admin-cs@st-andrews.ac.uk.

2024-2025 CS-EDI Poster Competition

The school EDI committee would like to run an EDI-themed poster competition during the academic year 2024-2025. The goal is to enhance EDI awareness and foster conversations around EDI topics. Posters aim to voice what really matters to you, and a means to celebrate the value of our diverse background and experiences. The selected posters will be displayed on the school walls and screens.

We welcome entries from all students in Computer Science. Participants can be teams or individuals. Prizes will be awarded to the winning teams. If you want to register interest to the competition or you have any questions, please contact edi-cs@st-andrews.ac.uk. More details can be found https://tinyurl.com/yx63a6sa. The deadline for expressing interest is 11 Oct 2024. We are looking forward to hearing from you.

Week 1 Social Events

The following social events are being held in the School this week in the Jack Cole coffee area:-

  • Monday 16 September – 17:00 – 18:00 – MSc and MSci welcome reception (TODAY)
  • Wednesday 18 September – 16:00 – 17:00 – Honours (Junior & Senior) welcome reception
  • Friday 20 September – 17:00 – 18:00 – Sub-honours (students on first and second years of CS programmes) social event

Drinks 🍷 and snacks 🍰 available at all events, so please come along and join us!

STACS Welcome BBQ 🍔

If you are a new Undergraduate or Postgraduate Taught student to the School of Computer Science, you are invited to the STACS Welcome BBQ outside the Jack Cole Coffee Area on Friday 13th September 5.30pm-7.30pm.

The usual BBQ favourites will be available from the grill and refreshments will be provided. We look forward to seeing you there!