PhD opportunities in Computer Science

Funding may be available for the projects below, if there is no specific funding mentioned with the project details please check the 'Funding' tab for further options.

We also welcome applications from students who already have funding in place.

For further information contact the relevant supervisor.

Projects in Intelligent Systems

Knowledge Graphs for Biomedical Image Data Management, Integration and Analysis

Supervisor: Albert Burger

Description: Biomedical images play a key role in research as well as clinical practice. A variety of imaging techniques are now available to study healthy as well as diseased tissue in human and model organisms, ranging from cell level to entire organs and organisms. In the clinical context, imaging diagnostics, such as MRI and CT scans, are now routinely used to better understand a patient's medical condition. To be able to take full advantage of the large image respositories that result from this, the information embedded in such images must be computationally represented. This is relevant for image retrieval, data integration and machine learning applications. Knowledge Graphs, a combination of graph-based data management and artificial intelligence, have emerged as a primary technique to model and capture such information, but many questions remain in terms of how to model the information in biomedical images and how to integrate and prepare them for machine learning based studies.  A variety of PhD topics are available in this area, including using knowledge graphs to model anatomical knowledge, integrating cinical image data sets across multiple patients, graph-based machine learning for biomedical images, and using Natural Language Processing techinques to integrate images with medical reports and publications.  If you are interested in discussing any of the above or other related topics for PhD study, please contact Prof Albert Burger.

Equally Safe Online – Multimodal Harmful Content Detection

Supervisors: Verena Rieser, Alessandro Suglia

Description: This project will develop novel multimodal algorithms to detect hateful and toxic content online automatically.  While current approaches solely focus on written text, this project will account for the fact that a large amount of social media posts involve several modalities (such as memes, videos) that carry information – which in their composition can result in harmful meaning. The objective of this project is to design and implement Deep Learning models for Multimodal Harmful Content detection that can be useful to moderate online content and make online spaces safer for Internet users.

Fair Graph Neural Architecture Search

Supervisor: Wei Pang

Description: This project will develop robust graph neural networks which can make fair and effective decisions. To achieve this, we will use multi-objective optimization techniques to make trade-off design solutions for graph neural networks. We will also explore the use of bio-inspired algorithms, such as evolutionary computing and immune-inspired computing algorithms to facilitate the search for suitable graph neural architectures. 

Vision-based surgical robot for interventional vascular surgery

Supervisor: Chengjia Wang

Description: Introduction of robotic procedures has brought significant benefits to intervention vascular surgery with remotely conducted precise procedures and reduced exposure to radiation for clinicians. In the meantime, renaissance of AI in the past decade has fundamentally reshaped many fields with escalated performance in computer vision, natural language processing and other tasks. However, many challenges exists in robot aided vascular surgery, for example, lack of annotated data, reduced sensor feedback, requirement of real-time intra-operation prediction etc.. In this project, you will explore the development of autonomous surgical robot which combining multi-modal sensor data to assist interventional vascular surgery. This involves investigation of frontier designs of surgical robotics tool, development and exploration of advanced deep learning models, fast fusion and visualisation of multi-modality sensory data (e.g., haptics, digital subtraction angiography, and other vascular imaging data), and a full understand of operational procedures in the treatment of vascular disease. 

Robot aid autonomous retrosynthesis for advanced drug discovery

Supervisor: Chengjia Wang

Description: As a fundamental problem in chemistry, Retrosynthesis is the process of decomposing a target molecule into readily available starting materials. It aims at designing reaction pathways and intermediates for a target compound. The goal of artificial intelligence (AI)-aided retrosynthesis is to automate this process by learning from the previous chemical reactions to make new predictions. Although several models have demonstrated their potentials for automated retrosynthesis, there is still a significant need to further enhance the prediction accuracy to a more practical level. This project aims to review, implement and review existing retrosynthesis methods and exploring the development of an autonomous verification robot for retrosynthecial process.

Decentralised, privacy-preserving and explainable federated learning for annotation effective medical image analysis

Supervisors: Chengjia Wang and Wei Pang

Description: Several infectious diseases have attacked human society since the beginning of Since the beginning of this millennium, in the meantime advanced AI models have been widely deployed to assist a variety of tasks for the fight against these global pandemics. Modern clinical studies, especially for popular disease that affect a large cohort of patients, are often requires a collaboration among multiple research centres with large-scale machine learning models applied to analyse distributedly stored clinical data acquired at different sites. Protection of privacy, effective model supervision and interpretability of prediction results are critical in clinical practices while a trade-off exists between any two of them. Furthermore, the required computing power for training the machine learning models can easily exceed the capacity of modern computing devices in clinical centres. This project aims to investigate training and deployment of decentralised federated learning scheme for large scale deep learning models with good adhoc or posthoc interpretability and minimal data exchange between sites. Specifically, the student will explore and development frontier research of image understanding, federated learning, decentralised semi- or weakly- supervise, model compression, heterogeneous data/model distillation, domain adaptation, XAI and possibly advanced blockchain technology.

Swarm bots and swarm intelligence in healthcare internet of things

Supervisors: Chengjia Wang and Wei Pang

Description: Internet of things offer a number of new opportunities to improve patient outcomes through real-time patient monitoring, advanced diagnostics, robotic surgery, and much more. However, the computing power of the associated IoT devices can limit the performance and scale and inference speed of adoptable machine learning models in practice. This is common in complicated robotic surgery and monitoring of high-risk patients, e.g., in ICU. In this project, we aim at development of swarm intelligence methods, empowered by modern deep learning models, that can coordinate a group of intelligent devices for event recognition and decision making. This involves deliberate management of computing capacity, communication, collaborated training and abnormality detection among a heterogeneous group of devices, and further developing the theoretical model into a group of well coordinated swarm bots.

Reference: C.Wang et al., Industrial Cyber-Physical Systems-based Cloud IoT Edge for Knowledge Distillation, IEEE-TII 2021

Projects in Interactive Systems

Safer Conversational AI

Supervisor: Verena Rieser

Description: Neural models for Conversational AI (ConvAI) and Natural Language Generation (NLG) are known to produce natural and fluent output, however the content is often bland, factually inconsistent, or inappropriate. This research will investigate how to create safer ConvAI models -- e.g. by developing advances in statistical modelling, data curation, evaluation metrics as well as wider design decisions, such as AI persona design.

This project is highly interdisciplinary and reaches from pushing the boundaries of state-of-the-art Machine Learning and statistical modelling to AI Ethics and anticipating the social impacts of AI. Applicants with an interest in either or both of these fields are encouraged to apply.

Generating counterspeech against online hate

Supervisors: Verena Rieser (1st), Ioannis Konstas (2nd)

Description: This project will develop countermeasures to effectively fight the ever-increasing online hate speech without blocking freedom of speech. In particular, it will develop Natural Language Generation algorithms to automatically generate counterspeech -- a response that provides non-negative feedback through fact-bound arguments and broader perspectives to mitigate hate speech and fostering a more harmonious conversation on social platforms. The project requires skills and expertise in deep learning frameworks, as well as knowledge of experimental design and data collection. The PhD project will be embedded in a large, multi-site and multi-disciplinary research project funded by the EPSRC.

Human-Robot Collaboration in Outdoor Environments

Supervisor: Phil Bartie

Description: Autonomous robots will offer the ability to support humans in outdoor environments via a wide variety of roles from rapid delivery of medical equipment via drones, to autonomous cars, to disaster emergency response. All of these need an interface to enable the user to set the robot’s goal, which often includes a destination location. This could be done via a mobile app (eg map interface) or web page, but a more natural way would be to use speech to converse with the device in setting a goal.  Human-robot interaction via voice enhances a robot’s ability to collaborate naturally and seamlessly with humans on joint tasks, e.g. by joint goal setting, communicating progress or clarifying the user’s intention. This research will focus on delivering the ability to generate and parse descriptions of 3D real world objects found in outdoor environments such as buildings, paths, parks, fields and other geographic features for the purpose of human-robot collaboration.

Grounded, Faithful and Transparent Natural Language Generation

Supervisor: Ioannis Konstas, Verena Rieser, Arash Eshghi

Description: This project will focus on the factual correctness of automatically generated text using Deep Learning. In particular, it will build Neural Natural Language Generation models with an emphasis on groundedness and faithfulness to the input source (e.g., news article, a question from a user, knowledge-base), as well as the ability to offer transparency and explainability in the form of attributions and reasoning rationales. The application domains we will explore can range from text summarisation to (conversational) question answering and will require skills and expertise in Natural Language Processing and deep learning frameworks such as PyTorch. 

Modelling Meaning Coordination in Situated Conversational AI

Supervisor: Alessandro Suglia, Arash Eshghi

Description: Human dialogue is rife with miscommunication because people often understand different things from words, have different perspectives, different cultural backgrounds, different skills, etc. The reason they manage to communicate successfully at all is that they put extra interactional effort to coordinate and make sure they understand each other sufficiently for current purposes; and that languages are universally equipped with highly systematised, meta-communicative procedures (known collectively as ‘repair’) such as clarificational exchanges, corrections and demonstrations. While the crucial role of these repair phenomena in sustaining a successful dialogue is widely recognised in Conversation Analysis and Cognitive Science, researchers in Natural Language Processing (NLP) and Human Robot Interaction (HRI) have paid relatively little attention to it so far. State of the art neural architectures that underly conversational AI systems today remain static at run time, don’t learn from the outcome of their actions, and are unable to handle miscommunication. From a Deep Learning perspective, it remains unclear how neural representations or word/sentence embeddings should be updated on the fly as a result of repairs and as part of a live interaction. Grounded in theories of miscommunication and repair in Cognitive Science, this project will investigate the suitability of existing neural architectures, objectives functions, and tasks for modelling meaning coordination in situated dialogue. If successful, the project will be an important stepping stone in the creation of Conversational AI systems that can successfully collaborate with humans in complex situated tasks. 

Projects in Rigorous Systems

Hardware designs for high level declarative, managed, programming languages

Supervisor: Rob Stewart

Description: The performance of functional programming language implementations until 10-15 years ago enjoyed ncreasing clock frequencies on uni-core CPUs. Now, parallel computing is the only effective way to speed up computation. Due to a single bus connection from multiple cores on a CPU to main memory, functional languages with parallelism support are finding the limits of general purpose CPU architectures.

In recent times, the fabric on which we compute has changed fundamentally. Driven by the needs of AI, Big Data and energy efficiency, industry is moving away from general purpose CPUs to efficient special purpose hardware e.g. Google's Tensorflow Processing Unit (TPU) in 2016, Huawei's Neural Processing Unit (NPU) in smartphones, and Graphcore's Intelligent Processing Unit (IPU) in 2017. This reflects a wider shift to special purpose hardware to improve execution efficiency.

This PhD project will conduct research alongside the funded EPSRC project HAFLANG ( ). The PhD student will have the choice on which architectural components to focus on. For example: 1) bespoke memory hierarchies for functional languages to minimise memory traffic where memory access latencies quickly become the bottleneck for real-world functional programs, or 2) lowering key runtime system components (prefetching, garbage collection, parallelism) into hardware to significantly reduce runtimes.

The aim of the PhD project will be to develop hardware/software co-design innovations in compiler construction and programmable hardware design that will 1) execute functional programs at least as twice as fast as on CPU architectures, and 2) consume four times less energy than CPUs when executing equivalent functional code. 

Runtime system innovations for non-strict managed functional languages

Supervisor: Rob Stewart

Description: High level functional languages offer the promise of programmer productivity, performance, and portability. Many languages hide hardware management from the programmer, e.g. memory allocation and evaluation order. A widely used language implementation is GHC, a software implementation of Haskell. The elegance of Haskell's non-strict evaluation strategy comes with a non-negligible overhead that presents a performance trade-off between avoiding unnecessary computation and runtime program management.

This PhD project will explore runtime system components that, if designed for modern architectures, have potential to significantly improve the efficiency of functional programming languages on stock hardware. The project will start by profiling cache misses, memory access latencies and memory contention to identify performance bottlenecks in real-world programs. The PhD project will use these profiles to design pre-fetching and locality aware garbage collection innovations for the GHC Haskell implementation to maximise the performance promises of non-strict evaluation strategies.

This project has significant potential for real-world impact with a large and growing Haskell community.

The project will complement the EPSRC HAFLANG project ( ), which is mapping these architectural design ideas into modern day FPGA hardware technology.

Compressing robust deep learning models for Edge Computing

Supervisor: Rob Stewart

Description: There is a growing trend moving deep learning models from expensive GPUs towards Edge Computing devices such as embedded CPUs, mobile devices and programmable hardware. Trained neural networks require hundred of Gibabytes of memory and high computation resources. Emerging compression techniques are able to reduce those resource costs significantly. This not only affects the accuracy of neural networks, but also their robustness properties that make them vulnerable to attack or catastrophic predictions.

This PhD project will explore the design space of compression techniques applied to deep learning models, guided by neural network verification techniques. There will be a strong focus on scaling this design space to state-of-the-art real-world applications of neural networks. There is potential for this PhD project to have strong industrial engagement.

The outcomes of this PhD project will inform the research community and industry practitioners on how to construct reliable and efficient deep learning models for application domains such image processing smart cameras, autonomous robot, and driverless vehicles.

Type systems for secure IoT and smart devices

Supervisor: Rob Stewart

Description: Programmable hardware is increasingly used in IoT and embedded systems for smart home devices and safety critical systems. The correctness of hardware designs is therefore critical. When hardware components communicate, they must do so via an agreed protocol. Many hardware vendors provide proprietary IP blocks for use in System on Chip (SoC) implementations. How are we to trust the communication behaviours of third-party IP components?

This PhD project will investigate session types from programming language theory as a mechanism to construct trustworthy SoC designs and to verify the runtime behavioural correctness of proprietary third-party IP hardware. This project will build on the outcomes of the EPSRC Border Patrol project ( ) .The project will develop mechanisms to translate session type descriptions into synthesisable monitoring hardware, and evaluate this technology on real-world SoC architectures. This PhD will be at the interface between programming language theory and real-world applications, with strong links with industry.

Zero-knowledge functional and parallel programming paradigms

Supervisor: Jamie Gabbay

Description: So-called zero-knowledge rollups (zk-rollups) work by executing computation on a (powerful) machine and delivering
* the result of the computation, and
* a cryptographic "certificate of honest execution" that the computation has been carried out correctly. 
What this means in practice is that computation in a distributed system (including but not limited to a blockchain) can safely be delegated, without every node in the distributed system having to run and re-run every computation.  They just check the certificates, which is simple and quick.

This is called "zero-knowledge (zk) computation" and "verifiable computation", and it is a mature field of research.

However, so far as I know what constitutes "computation" in this context has not been well-researched.  The models of computation used in practical zk systems are single-threaded Turing machines.  This works for some applications but breaks down if we require more powerful programming paradigms, such as functional programming (like Haskell, OCaml, Idris, or Agda), or inherently parallel computations (such as multiplication of large matrices).

The research goal can therefore be summed up as follows: given that "zk-Turing" exists, create "zk-Haskell" and "zk-CUDA".

Nominal tensor programming

Supervisor: Jamie Gabbay

Description: Nominal techniques were originally developed to model programming on syntax with binding (nominal abstract syntax), and have since been applied to other domains, most notably to automata (nominal automata).

A very natural next step is to examine graphs and tensors.  The act of connecting one node to another is viewed as a binding operation, where (literally) connecting two nodes binds those nodes together, but using nominal techniques this has a precise mathematical meaning.

The starting point of the project is to develop a theory of nominal graphs and tensor structures, and apply this in the mathematical fields of the candidate's choice.

Verification of AI/ Foundations for Verified AI

Supervisor: Ekaterina Komendantskaya

Description: Predictions and judgements made by machine learning algorithms play an increasingly important role in complex intelligent applications in society-spanning critical sectors such as economics, manufacture, energy, legislation, healthcare, and security. Because of the impact of these decisions, it is of central importance to understand what factors contributed to a decision, or whether trust in a decision can be significantly improved.

Historically, a particularly influential approach is Valiant’s learnability theory, which in the 1980s gave abstract theoretical foundations for supervised machine learning. These foundations provided theoretical methods to bound accuracy of classifiers on the training data, and measure how they generalise to yet unseen examples.
However, a more recent realisation, due to Szegedy et al., is that even complex ("deep") networks with
high accuracy and generalisation capacity fall victims to adversarial attacks (extremely small perturbations
to the inputs). This is not just a security issue for practical applications of neural networks, but it uncovers
a much bigger problem: accuracy and generalisation measures do not characterise how well the network learnt
“semantic meaning” of classes. Subsequent research introduced different ways to train networks to be robust
(e.g. data augmentation, adversarial training), which however were still vulnerable to a strong enough attack.
Moreover, it was shown that other kinds of ML have the same vulnerabilities, notably reinforcement learning,
recurrent networks and transformers. There is a growing understanding within the community that machine learning needs new foundations in order to tackle this problem of AI verification.

At the same time, the well-established verification technologies (SMT solvers, model checkers, higher-order interactive theorem provers) that have seen successes in standard software verification often fail to apply out-of-the box in machine learning scenarios. Lab for AI and Verification ( at Heriot-Watt is a group of reserachers working on better understanding of the verification challenge that complex intelligent systems pose and on methods of adapting logic, functional programming, type theory methodologies in this new domain. See our webpage for examples of concrete projects and contact to discuss how your interests fit with LAIV.

Parallel runtime systems for high-performance machine learning

Supervisor: Hans-Wolfgang Loidl

Description: Parallel computation offers enhanced compute power by using a large number
of independent compute units to solve a problem. Exploiting the potential
of parallelism can be challenging when using low-level programming models
such as C+MPI. In contrast, high-level languages, such as Haskell, provide
abstractions to more easily implement parallel programs. Mapping these high-
level abstractions down to the machine is the role of the runtime-system, and
it has a lot of freedom how to efficiently arrange the parallel execution.

The core topic in this PhD is to develop and enhance parallel runtime systems
for high-level languages, such as Haskell, Java, or Cilk, and improve the
performance of parallel execution on multi-core servers as well as on compute
clusters. The key challenges are to develop efficient scheduling and distribution
technologies that work across a range of programs, and that are managed
automatically by the runtime-system. The main application domain for these
runtime-systems will be modern machine-learning algorithms with their high
demands on data to be processed and compute units that should be exploited
to make the ML technique feasible in real-world scenarios.

Algorithms in parallel symbolic computation

Supervisor: Hans-Wolfgang Loidl

Description: Symbolic computation covers a range of areas that apply mathematical
knowledge to provide solutions for whole classes of problems. These
typical focus on symbol manipulation over complex data structures,
rather than numerical computation over flat structures such as matrices.
Prominent sub-areas are computer algebra, with systems such as Maple or
Mathematica, and automated theorem proving, with provers such as Isabelle
or Coq. More recently this area is merging with the programming language
area by supporting reasoning-oriented language features such as dependent
types, with systems such as Agda.

The goal of this PhD thesis is to examine several commonly used symbolic
algorithms and to investigate re-designs and re-implementations of these
algorithms to cater for massive parallelism as is readily available now.
The focus in this work could be on applying mathematical knowledge to re-
design algorithms, using high-level language constructs to facilitate the
parallelisation of existing algorithms, or on the verification of correctness
and resource properties using modern verification techniques.

Type Error Diagnosis for Type Level Programming

Supervisor: Jurriaan Hage

Description: The Helium compiler for Haskell (see is a compiler that specializes in providing good type error diagnosis. Over the 2 decades since its inception we have made lots of progress, including work on GADTs and preliminary work on type families, but a few important parts of the Haskell programming language still need to be investigated: impredicative polymorphism/higher ranked types, and the many flavours of type classes that Haskell supports. Your task as a PhD is to consider these language extensions, implement them into the compiler, design heuristics, and validate the quality of these heuristics. Familiarity with Haskell is a definite plus, but do not worry if you have never used Haskell's many language extensions before.

Higher-rank polyvariance in static analysis of functional languages

Supervisor: Jurriaan Hage

Description: Higher-rank polyvariance (akin to higher-rank polymorphism in type systems) is a form of high precision for static analysis of functional languages. It is still unclear where the boundaries of decidability lie exactly, and your task to research where these lie more exactly by considering a number of static analyses and investigating what happens in these cases. Implementations can be made in the Helium compiler (, or standalone implementations.

Another important aspect of higher-rank analysis is to ways to prove soundness of the analysis, preferably by mechanizing the meta-theory in Coq or some system like it.
Although implementation will form a sizable chunk of the work, this project makes high demands on your facilities of abstraction and your understanding of logical deduction systems, and is therefore quite mathematical in nature.


SDN-based MANETs Using Existing OpenFlow Protocol

Supervisors: Idris Skloul Ibrahim and Lilia Georgievea

Description: There is a continuous business need for network technologies to increase functionality, performance, and complexity. However, the present network paradigms show a lack of adaptability and are limited to single-domain management. Thus, management of the network places a burden on the network’s users. In addition, the high number and variety of stationary or dynamic devices make the network massive and intractable, with a complexity that leads to scalability challenges. Modern requirements cannot be supported by the current decentralized mobile ad hoc networks (MANETs) standard models. Additionally, MANETs suffer from packets/network overheads due to topology changes with the distributed and (decentralized) routing in each node. In a typical architecture, the mobile node is responsible for dynamically detecting other nodes in the network. The node can communicate directly or via an intermediate node, and specify a route to other nodes. Thus, the node takes a decision with only a limited view of the network’s topology. To this end, the deployment of the Software Defined Networking (SDN) paradigm has the potential to enable the redesign of these models. SDN provides a global view of network topology and a programmable network with centralized management. In this paper, we propose a new architecture for SDN-based MANETs, which is adding an Open Virtual Switch (OVS) per node to find the effect of OVS on the MANETs performance. We present a practical implementation for the new architecture using the existing OpenFlow protocol. The tests have been carried out in an emulation environment based on Linux Containers (LXC V 2.0.11), Open Network Operating System (ONOS V 2.5.0) as a remote controller, NS3, and Open Virtual Switch (OVS V 2.5).


EPSRC Centre for Doctoral Training Award (Robotics and Autonomous Systems)

The Centre's main programme is the EPSRC Centre for Doctoral Training in Robotics and Autonomous Systems. Its goal is to train innovation-ready robotics researchers to be part of a multi-disciplinary enterprise, requiring sound knowledge of physics (kinematics, dynamics), engineering (control, signal processing, mechanical design), computer science (algorithms for perception, planning, decision making and intelligent behaviour, software engineering), as well as allied areas ranging from biology and biomechanics to cognitive psychology.

Find out more information about the EPSRC Centre for Doctoral Training in Robotics and Autonomous Systems.

EPSRC Centre for Doctoral Training Award (Embedded Intelligence)

Heriot-Watt University and Loughborough University are jointly offering a unique 4-year PhD training programme, drawing on their considerable expertise in postgraduate teaching and research supervision in the fields of sensors, system design, embedded software and systems, applications engineering and systems services.

Embedded Intelligence is characterised as the ability of a product, process or service to reflect on its own operational performance, usage load, or in relation to the end-user or environment in terms of satisfactory experience. This self-reflection, facilitated by information collected by sensors and processed locally or remotely, must be considered from the design stage such as to enhance the product lifetime and performance, increase quality of process or service delivery, or ensure customer satisfaction and market acceptance. For more detailed information, please visit Embedded Intelligence CDT or contact Professor Mike Chantler.

EPSRC-funded PhD studentship

Heriot-Watt receives in the region of £1.5M per annum from the UK Research Councils to fund PhD research students. Awards to individuals comprise payment of fees and a non-taxable stipend of around £14,500 per annum. This is available to suitably qualified UK nationals and non-UK nationals if resident in the UK for 3 years prior to the course. Other EU students can apply to have fees paid only although Schools may be able to provide a partial stipend for highly qualified EU applicants.

Information about research activities in each area can be found on the relevant group websites.

Further enquiries should be addressed to Professor Mike Chantler.

How to apply

Informal enquiries should be made to Professor Mike Chantler.

You can find full details of how to apply.

If applicable, please specify on the application form for which funding option you are applying.

Key information

School of Mathematical and Computer Sciences