PhD

World-Leading PhD Scholarship: Personalised, Adaptive, Language-based Planner for next generation Robotics

A fully-funded PhD scholarship in Computer Science is available for a strong and motivated student wishing to work at the intersection of vision, large-language models, and robotic planning. This prestigious PhD scholarship is awarded by St Leonard’s Postgraduate College at the University of St Andrews and will be supervised by Dr Juan Ye, Dr Alice Toniolo, and Dr Kasim Terzic.

For more information, including how to apply, please see the advert: https://www.st-andrews.ac.uk/study/fees-and-funding/scholarships/scholarships-catalogue/postgraduate-scholarships/world-leading-scholarship-01-computer-science/?

Fully-funded PhD scholarship in Health Data Visualisation

The School of Computer Science at the University of St Andrews has a fully-funded scholarship available working with Dr Areti Manataki. The PhD topic is “Shedding light on patient flow through advanced data visualisation”. Applications must be received by 1 March 2023.

Project Overview

In modern healthcare systems, millions of patients are admitted to hospital every day. Managing patient flow through hospital, to ensure that patients are at the right place at the right time, can improve quality of care and health outcomes, while saving money and time. However, managing patient flow in a way that is safe for patients and cost-efficient is a challenging task, and requires a deep understanding of the complexities associated with patient flow.

This project involves employing advanced data visualisation techniques to shed light on patient flow and its many important dimensions: temporal and spatial patterns, patient characteristics, clinical expertise, hospital capacity and associated cost. Drawing inspiration from visualisation approaches to astronomy and transportation, and working closely with healthcare professionals, we will develop interactive visualisations that allow for the exploration of large and rich patient flow data. Our aim is to build visualisations that are powerful enough to capture the complexity of patient flow, and, at the same time, simple enough for clinicians to easily use to draw conclusions towards improving care.

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 (e.g., Data Science, Engineering, Mathematics, etc.). Experience in data visualisation, enthusiasm for research at the intersection of data science and health, an ability to think and work independently, excellent programming and analytical skills, and strong verbal and written communication skills are essential.

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

To apply

Informal inquiries can be directed to Dr Areti Manataki at A.Manataki@st-andrews.ac.uk. Formal applications can be made through the School’s postgraduate research portal.

The deadline for applications is 1 March 2023.

Funding Notes

We have one fully-funded scholarship available, which will be awarded competitively to the best applicant. This scholarship covers all tuition fees irrespective of country of origin and comes with a stipend (currently £17,668 per annum full-time equivalent). Additional scholarships may be available from other sources.

The School welcomes applications from under-represented groups, and is willing to consider part-time and flexible registrations. The successful applicant will however be expected to conduct their research in St Andrews and not fully remotely.

Fully-funded PhD scholarship in complex systems and simulation

The School of Computer Science at the University of St Andrews has a fully-funded scholarship available working within the Complex and Adaptive Systems Research Group with Prof Simon Dobson and Dr Peter Mann. Applications must be received by 1 March 2023.

Background

A “complex” system is one in which cause and effect can be hard to determine. In an epidemic, for example, it is easy to determine how someone was infected (by one of their social contacts), and we can also predict the overall size of an outbreak from the properties of the contact network — but we may not be able to predict in detail how the epidemic proceeds through the network, or what could be done to counter or steer it. Other examples include studying how rumours grow (and can be countered) on social networks, or to understand the effects of placement and error in sensor-driven systems such as those in climate science and ecology.

We understand relatively little about how things at “in-between” scales affect processes. These “meso-structures” include things like dense clusters of individuals, sparse chains of contacts, networks with core and periphery structures with different properties, and so on.

We are conducting a research programme investigating network meso-structures, with several goals. We want to understand these structures’ effects both analytically and numerically, meaning that we want to develop new frameworks for network process simulation and modelling based on our locally-developed simulation framework, epydemic, and to develop new analytic approaches to the study of these topics based on ideas from simplicial topology and sheaf theory.

Topics of interest

We are interested in a lot of different approaches, including but not limited to:

  • Applications of generating functions to the study of network processes
  • New applications of discrete combinatorial mathematics to complex systems
  • Understanding the effects of fine structure on processes
  • New simulation and numerical analysis techniques for complex systems
  • Epidemic spreading, especially the ways in which disease variants interact and develop through co-infection
  • Complex contagions such as rumour-spreading
  • Generating random networks with specific statistical properties

The scholarship

We have one fully-funded scholarship available, which will be awarded competitively to the best applicant. This scholarship covers all tuition fees and comes with a stipend (currently £17,668 full-time equivalent). Additional scholarships may be available from other sources.

The School welcomes applications from under-represented groups, and is willing to consider part-time and flexible registrations. The successful applicant will however be expected to conduct their research in St Andrews and not fully remotely.

To apply

Informal inquiries can be directed to Simon or Peter. Formal applications can be made through the School’s postgraduate research portal.

The deadline for applications is 1 March 2023.

A fully-funded, PhD position is now available at the AI research group

This fully-funded position will aim to improve both the modelling capabilities and the solving performance when confronting Automated Planning problems. We seek motivated candidates with a strong background in Computer Science, with excellent programming skills and some previous knowledge and experience in solving combinatorial optimisation problems.

Please take a look at the instructions on on how to apply.

The University of St Andrews is the top university in Scotland and second in the UK in The Times and Sunday Times Good University Guide 2023. Last year St Andrews was ranked number one in the UK, the first time in the near 30-year history of the Guide, or any UK ranking, that any university has been placed above those of Oxford and Cambridge.

If you are interested in either knowing more or have any informal enquiry, please do get in touch with Joan Espasa Arxer via email: jea20@st-andrews.ac.uk

The deadline for applications is the 1st of March 2023. 

Fully funded PhD scholarship: Trustworthy Refactoring Tools for Haskell Programs

Supervisor: Dr Christopher Brown

Proposal and Context

Software is large and complex. Ubiquitous systems, such as weather forecasting, medical imaging, advanced AI and big-data processing are extremely expensive and time-consuming for software companies to produce. Moreover, they often comprise many subtle bugs that can have disastrous consequences, are difficult to find, and difficult or impossible to fix. What developers need are specialised software refactoring tools that help them develop these important and complex systems in a safe and semi-automated way, reducing developer time, human error and overall increasing productivity, saving companies and customers money, and providing robust, safe, systems that have been developed in a responsible and trustworthy way.

Refactoring is the process of changing the structure of software without changing what it does: in effect, refactoring is about helping the programmer re-purpose their code to make it more understandable, accessible, or amenable to further change in the program’s design. It is often a process that developers use on a daily basis by manually changing code to reflect an API change, re-purposing methods, eliminating duplicated code, remaining variable parameters and function names, generalising functions, etc.  However, this process is rather tedious and cumbersome to apply manually: effecting a structural change that could potentially affect millions of lines of code across thousands of files is inevitably error – prone.

The advent of refactoring tool-support provides developers with automated transformations that they can apply to their code base, usually through an existing IDE interface. Refactoring tools, on the other hand, provide a way to apply refactorings across an entire code base in a semi-automatic way: they rely on the user to make certain choices about which refactorings to perform but are automatic in their underlying machinery. This automated underlying machinery means that both simple and complex refactorings can be applied to large code bases comprising thousands of files and millions of lines of code instantly.

However, automated tools are prone to bugs. This has the potentially disastrous consequence of a refactoring tool refactoring a program into one that contains subtle bugs or changes in behaviour. Despite the obvious implication that this will refactor software to contain, perhaps, subtle and difficult-to-spot bugs, it also erodes developers’ confidence in using refactoring —and general software tools— in general. Furthermore, a refactoring that introduces errors or is not trustworthy requires the programmer to inspect the transformed code, therefore taking out the benefit of using an automated tool in the first place. The problem is also amplified in that refactoring tools are extremely cumbersome, laborious and difficult in themselves to implement, especially over large programming languages, such as Haskell. This makes the refactorings deployed in such tools limited, both in number and applicability.

What is needed is to answer the following research questions:

  1. How can we provide an automated approach of implementing refactoring tools, via compositionality and proof search?
  2. How can we have the means to generate new refactorings easily that are safe and trusted by the developers that require them?
  3. How can we provide a means to generate soundness proofs that the refactorings are safe and verified, in an automated way?

Exploratory Ideas

The PhD will be exploratory in nature. Here are some ideas for research directions to investigate as part of the PhD:

  1. Formally characterise a number of refactorings for Haskell and prove properties of their general soundness.
  2. Develop a fully verified refactoring tool that encodes general soundness proofs as part of its implementation using e.g., Dependent Types for the full Haskell standard.
  3. Model a fully verified static and functional semantics for Haskell using e.g., Dependent Types.
  4. Provide a fully generalised theory of the formalisation of refactoring tools for e.g., Haskell.
  5. Provide an automated technique to find refactorings and proofs of refactorings, via e.g., proof search and compositionality of sub-proofs.
  6. Evaluate the applicability of the approach on a number of use-cases and domains, from a variety of languages, across a different number of verticals.

Supervisor and Background

The PhD project is to be supervised by Dr Christopher Brown, a lecturer in the school of computer science who has over 18 years of experience working in the field of refactoring, program transformation and functional programming. Dr Brown contributed to the original HaRe (Haskell Refactorer) system, developed at the university of Kent, and has since developed a number of new refactoring tools for a variety of languages, including Haskell, Erlang and C/C++ for introducing and tuning parallel programs. Dr Brown also works in the field of formal semantics, with prior work on formalising refactorings and using types to reason about e.g., extra functional properties of imperative systems for embedded languages.

The PhD project fits directly with the research vision of the supervisor, who is also working on building refactoring tools for dependently typed languages, such as Idris and Pi-Forall.

The Programming Languages Research Group

The Programming Languages Research Group has a long history in functional programming and type theory. This project would directly fit with the group’s general vision of:

  1. Making programming languages more accessible to experienced and inexperienced programmers alike.
  2. Providing tool-support to make programming more accessible to inexperienced programmers.
  3. Using types to formalise general soundness of programming language properties and semantics.
  4. Providing verified refactoring tooling for functional programs.

To apply

Informal inquiries can be directed to Chris Brown. Formal applications can be made through the School’s postgraduate research portal.

The deadline for applications is 1 March 2023.

PhD success for a former graduate

Last month Professor Simon Dobson was invited to be on the PhD examining committee for Indushree Banerjee at TU Delft.

PhD examining committee, paranymphs, and family

She passed with flying colours, for her thesis on ad hoc network protocols for use in disaster recovery situations. The protocol is designed around a very strong model of social justice and equality, working on low-power mobile devices and operating so as to conserve power reserves and device lifetime over the important 48-hour initial period of disaster relief.

Indushree did her MSc in St Andrews ten years ago, which gives us the opportunity for a couple of before-and-after photographs.

Simon and Indushree, MSc graduation 2012 Simon and Indushree, PhD graduation 2022

Neither of them seem to have changed all that much, apart from Simon having gone “Full Gandalf” during lockdown.

Indushree is now doing a postdoc in Delft, focusing on technology applied to  wildlife conservation and ecology. We’re hoping to get her over for a seminar in the new year.

PhD opportunity: Interdisciplinary approaches for improving biodiversity assessments using remote sensors

Funded PhD opportunity.

The project will be heavy on state of the art machine learning with applications to statistical ecology. The project will be jointly supervised by the Schools of Maths and Stats (Alison Johnston, Chris Sutherland) and Computer Science (Kasim and Oggie) and potentially fully funded by CREEM.

Strong applicants with experience with deep learning and strong maths background are particularly welcome to apply.