Current PhD Projects

Here you will find information on the individual PhD research projects that our students are working on with our project partners and academic supervisors.

 Cohort 1

PhD Student
Project PartnerProject title and description
Emmanouil Pitsikalis Denbridge Marine

'Scalable Track Analytics'

Nowadays, shipping is one of the most important industries that undeniably requires increased security and safety. Both security and safety can be improved through maritime analytics applied on the massive amount of currently available heterogeneous maritime data. Using data from multiple sources, such as CCTV, AIS and RADAR, tasks such as vessel type classification, vessel behavioural analysis, and anomaly detection can effectively be addressed. In this project we focus on ship type classification using novel methods and scalable maritime analytics over AIS and RADAR streams.

Download here: Scalable Track Analytics 

Julia Kolaszynska MBDA

'Coordination and Cooperation in Adversarial Engagements'

This project will develop methods to provide tactical guidance and decision making for future air defence systems. The aim will be to provide an operator that is robust, timely and that optimises the defensive responses across all available assets; including platform/vehicles, countermeasures and inceptors.

Download here: Coordination and Cooperation in Adversarial Engagements 

Konstantinos Alexandridis Vision4ce

'Drone Detect and Desist'

Due to the increased use of drones for recreational and commercial purposes, safety measures need to be taken to ensure airspace and civilian security. Drone detection can solve these problems by applying video processing techniques and artificial intelligence. When creating such systems, there are many challenges to consider such as, harsh environmental conditions, obscure sceneries, long operating duration and low energy consumption. This research is fascinating because it involves interdisciplinary studies and is useful and practical.

Download here: Drone Detect and Desist 

Marco Fontana Sintela

'High Performance Processing of Distributed Acoustic Sensing Data: Turing Optical Fibers into Massively Parallel Microphone Arrays.'

The project is about the development of new detection and tracking algorithms to apply to optical fibre sensors. The idea is to turn Distributed Acoustic Sensor (DAS) into an array of microphones and to use the data provided to monitor highways, railways, and critical infrastructures, like oil pipelines. A new Bayesian signal process chain based on machine learning algorithms will improve the performance and decrease the complexity of the algorithm in a real-world scenario.

Download here: High Performance Processing of Distributed Acoustic Sensing Data 

Matthew Carter IBM

'Uncertain Heterogeneous Algorithmic Teamwork'

This project is focused on showing that raw aggregated capability can outperform carefully constructed co-ordination. Specifically, there are situations where vast quantities of computational hardware in a heterogeneous environment can outperform dedicated high-performance resources in a carefully constructed homogeneous environment. This PhD project will investigate whether it is possible to adapt a divide-and-conquer algorithm (which is at the heart of set numerical Bayesian techniques) to operate efficiently in an uncertain heterogeneous environment.  The aim is to develop the infrastructure that makes it possible for vast heterogeneous computing resources (e.g. desktop PCs, GPUs and android phones) to operate effectively in a team.

Download here: Uncertain Heterogeneous Algorithmic

Theofilos Triommatis MBDA

'Distributed Exploration and Exploitation with Passive RF Sensors'

This PhD project is about cooperative navigation of unmanned air vehicles (UAVs) and exploration of an area using passive RF (radio-frequency) sensors under various constraints and reachability objectives. Passive RF sensors pick up the signal that an object emits and provide information about its angular position and signal strength. There are many challenges in this project depending on its setting, for example, in a search and rescue scenario, under hazardous weather conditions, the goal could be to search an area quickly, by avoiding damaging weather and at the same time maximising the probability of finding survivors. We can identify the following two questions that are of interest here, "how fast can several UAVs complete the search of multiple objects with a mathematical guarantee?" or "is there an efficient protocol to guarantee safe and reliable communications under several constraints?".

Download here: Distributed Exploration and Exploitation with Passive RF sensors

Vincent Beraud UK Gov

'Fast Bayesian Deep Learning'

Deep learning has become one of the fields of Artificial Intelligence research that raises great interest in machine learning researchers due to its very high performance in specific tasks. However, deep learning encounters difficulties in many fields due to its absolute output certainty which might be detrimental for decision-making tasks. The goal is to couple Bayesian methods with deep learning to build complex models with uncertainty measurements. The current implements of Bayesian neural networks are accurate OR fast, the objective now is to meet both these aspects using new sampling methods.

 Cohort 2

PhD StudentProject PartnerProject Title and Description
Adam Lee GCHQ

'Fast Fully Bayesian Gaussian Processes'

This project is focussed on adapting Gaussian Processes for the age of Big Data, not only in terms of scalability of the method but also in limiting the need for non-autonomous interaction. The need to specify both mean and covariance functions, which then need to be tuned such that they best represent the training data, limits a Gaussian Process' ability to learn from the data in an unsupervised manner. Recently, there has been a resurgence in research into scalable approaches for Gaussian Process regression from the wider community, both in improvements to existing algorithms and in exploitation of modern computer architecture. The scope for improving the scalability of GPs further while also integrating the possible ideas of an autonomous statistician gives way for this project.

Download here: Fast Fully Bayesian Gaussian Processes 

Benedict Oakes  DSTL

'Scheduling Surveillance of Space Objects'

The aim of this project is to develop cutting edge sensor surveillance strategies to observe space objects which can outperform existing techniques by developing high quality, efficient, non-myopic sensor management algorithms to control space-surveillance sensors. These algorithms are required to maximise the value gained from limited sensor resources to enhance the understanding of the composition of space objects in orbit, specifically using ground-based optical telescopes. These surveillance strategies should have the ability to detect and respond in the instances that a satellite manoeuvre was performed and/or a collision event has occurred, to ensure that the correct data is promptly collected to enable mitigation actions.

Download here: Scheduling Surveillance of Space Objects

Efthyvoulos Drousiotis National Crime Agency

'Data-driven Intelligence for Countering Crime'

Organised crime is one of the greatest threats to the UK’s national security. The role of the National Crime Agency (NCA) is to protect the public by disrupting and bringing to justice those serious and organised criminals who present the highest risk to the UK. However, the volumes of data are growing exponentially. There is therefore a need to work with these experts to automate the processing of the data, learn what these hallmarks are and then search the data for instances worthy of further human analysis. The need is growing for such data-driven intelligence.

Download here: Data Driven Intelligence for Countering Crime

Elinor Davies   Leonardo

'Learning to See More: Better Bayesian Track Before Detect using Statistical Machine Learning'

This project is concerned with improving the ability to detect faint objects in, for example, radar data. By improving detection performance, cheaper sensors will be able to emulate more expensive sensors, making the development of advanced detection algorithms very important in industrial settings. Leonardo develops such sensors.  The key challenges are in developing real time processing methods for distributed processors that can use low-power processor systems and using adaptive scheduling to maintain energy efficiency across a number of processors.

Download here: Learning to See More Better Bayesian Track Before Detect 

Jack Wells Sivananthan Laboratories

'Machine Learning to Identify Unique Events in Sparse Hyperspectral Datasets'

The goal of this PhD project is to investigate machine learning approaches that may permit images from stable materials, obtained from a wide variety of methods, to be used to increase the ability of methods such as hyperspectral imaging in electron microscopy and X-ray systems to observe thermodynamically unstable materials and processes on the atomic scale. Such advances have the potential to significantly impact the search for new personalized medicines, the development of new advanced energy storage systems, and our ability to directly see chemistry important for catalysing environmentally friendly processes.

Download here: Machine Learning to Identify Unique Events in Sparse Hyperspectral Datasets 

Jordan Robinson DSTL

'Distributed Hypothesis Generation and Evaluation'

Approaches should be developed to allow analysts with particular expertise to contribute their knowledge to different parts of an intelligence analysis in a collaborative manner by generating sub-arguments which are coherent with the analysis as a whole. Analysts or agents, using a well-defined theory of abstract argumentation, may then evaluate the hypotheses, which may suggest further collection of information or require refinement of hypotheses. Intelligence analysis should be considered as a cycle and therefore all stages of the process should be compatible with collaborative and distributed analysis. The approaches developed should be validated against other intelligence analysis techniques. Care will be needed to mitigate the potential for biases in the system.

Download here: Distributed Hypothesis Generation and Evaluation'

Mehdi Anhichem    Aircraft Research Association

'Towards Data Driven Aerodynamic Models: Data Fusion of Experiment and Simulation'

The digital age with ubiquitous physics-based computational engineering tools, such as computational fluid dynamics (CFD), machine learning algorithms and ever-increasing computing power, helped accelerate the development of novel technologies deployed in the civil transport sector, as well as in defence and security, to meet the most demanding economic, environmental and societal challenges.  It is envisaged to first explore future algorithms, including AI surrogate models, for near real-time joint experimental/numerical data analysis, that is uncertainty-aware, robust and quantifiable, to inform and optimise a wind-tunnel campaign, including on-the-fly. Second, considering the vast amount of data that a high-fidelity CFD run and a fully instrumented wind-tunnel test can produce, particularly for unsteady flow simulations, the first objective calls for high parallelisation utilising future computing systems, such as those explored within this CDT.

Download here: Towards Data Driven Aerodynamic Models 

Oisin Boyle    Gencoa

'Extracting Important Information from Noisy Spectra'

The aim of this project is to develop algorithms that are both fast and accurate. The proposed approach is to speed up mature numerical Bayesian algorithms that are already accurate. These algorithms are accurate because they search over the space of all combinations of peaks that could be present in each spectrum. The algorithms explicitly compare the measured spectrum with those associated with each combination and also explicitly consider the extent to which the combination is consistent with prior knowledge (Gencoa are developing libraries to make it possible to predict which set of peaks would be likely to co-occur; a single impurity will typically give rise to multiple peaks). This approach makes it possible for the algorithms to make accurate inferences about whether a bump in the noise-floor or on the side of a large peak is caused by chance or by the presence of a low-amplitude peak.

Download here: Extracting Important Information from Noisy Spectra

Panagiotis Pentaliotis National Crime Agency

'Data-driven Intelligence for Countering Crime'

Organised crime is one of the greatest threats to the UK’s national security. The role of the National Crime Agency (NCA) is to protect the public by disrupting and bringing to justice those serious and organised criminals who present the highest risk to the UK. However, the volumes of data are growing exponentially. There is therefore a need to work with these experts to automate the processing of the data, learn what these hallmarks are and then search the data for instances worthy of further human analysis. The need is growing for such data-driven intelligence.

Download here: Data Driven Intelligence for Countering Crime 


 Cohort 3

PhD Student Project PartnerProject Title and Description

Chris Blackman     


Machine Learning for Target Detection and Classification Using Multi-modal Airborne Sensor Data’

This PhD will seek to establish whether multi-modal airborne sensor data, used early in the signal processing chain, can be used to detect and classify objects earlier in the processing chain leading to improved detection and classification performance.

Download here: Machine Learning for Target Detection and Classification 

Alex Bird  Dstl 

‘Scheduling of Distributed Information Processing’

The focus of the PhD will centre on developing statistical emulators that can predict how computation will process data and generate information. Once those emulators exist, the focus will be on using the emulators to schedule distributed computational resources.

Download here: Scheduling of Distributed Information Processing.
Oliver Dippel NSG

‘Bayesian Reinforcement Learning for Control of Continuous Industrial Processes’

This project is focussed on finding a good control strategy to optimize the quality of a glass product during a specific manufacturing process or reducing the cost of this process through less faulty goods. Ideally, both goals may be combined. The widely used Proportional-Integral-Differential (PID) controller should thus be replaced or expanded at best. Although the PID controller is widely used, it has its weaknesses and sometimes requires human intervention. To overcome them Bayesian Inference will be used to generate simulated data based on offline historic data from the manufacturing process.

Download here: Bayesian Reinforcement Learning for Control 


Jinhao Gu



‘Artificial Intelligence for Fast Discovery of Novel Materials for Healthcare’

This PhD will incorporate the following elements: (1) Review of existing approaches to using Gaussian Processes to solve regression problems involving descriptions of molecules as the features and existing techniques for articulating similarities between different molecules and/or sets of molecules; (2) Development of scalable Gaussian Process implementations (e.g. involving variational or distributed approaches to representing the uncertainty) and hyper-parameter estimation (e.g. using novel Sequential Monte Carlo methods) that exploit emerging many-cored compute resources to facilitate timely performance; (3) Application of these approaches in the context of Bayesian Optimisation to help answer questions pertinent to the discovery of novel materials for healthcare.

Download here: Artificial Intelligence for Fast Discovery of Novel Materials for Healthcare


 William Jeffcott


‘Data Science and Artificial Intelligence for Smart Sustainable Plastic Packaging’

High-density polyethylene (HDPE) is widely used in plastic production. It can be recycled to produce a post-consumer resin (PCR), which can then be used to make new plastic products. However, this PCR may contain differing grades of plastic, and may become contaminated with other materials. These factors affect the performance of the end product. At present, the more favourable solution for companies is to make new virgin plastic rather than recycling.

Download here: Data Science and Artificial Intelligence (Plastics) 

 George Jones


‘Non-Myopic Approaches to Sensing and Surveying’

A key focus of the project is likely to be understanding the potential for exploiting multi-core computing hardware such as GPUs and/or FPGAs. Another area of interest is how such approaches can generate solutions as part of a human-machine teaming concept, in order that they promote trust and transparency and help end-users understand the inherent trade-offs in the optimisation process.

Download here: Non-Myopic Approaches to Sensing and Surveying 

Andrew Millard



‘Scalable Online Machine Learning’

This project relates to extending the state-of-the-art to enable machine learning to fully capitalise on the information present in never-ending data streams. The additional data that arrives over time contains information that should facilitate improved machine learning. Not using this information gives rise to consistent yet surprising errors: this typically occurs when the training data is small relative to the algorithm’s empirical experience.

Download here: Scalable Online Machine Learning 

Kieron McCallan


‘Using Next Generation Machine Learning and AI techniques to Aggregate Information Pertinent to Automotive Glazing’

Simulation techniques exist to assess how feasible it is for existing production processes to be used to make a new glazing part for a car. While instances of these simulations have been run extensively in the past, it is unlikely that the new glazing part will be identical to one that has been assessed previously. 

Download here: Using Next Generation Machine Learning and AI Techniques

Joshua Murphy


‘Scalable Online Machine Learning’

The project relates to extending the state-of-the-art to enable machine learning to fully capitalise on the information present in never-ending data streams. The additional data that arrives over time contains information that should facilitate improved machine learning. Not using this information gives rise to consistent yet surprising errors: this typically occurs when the training data is small relative to the algorithm’s empirical experience.

Download here: Scalable Online Machine Learning

Jack Taylor 

Sivananthan Laboratories 

‘Optimized Sampling Approaches for Compressive Sensing in Multi-Dimensional Datastreams’

The goal of this PhD project is to determine the minimal level of sub-sampling that will be sufficient to reconstruct images from transmission electron microscopes. By developing and implementing new compressive sensing algorithms to tackle the challenge of hyperspectral data, the ultimate goal is to develop a coherent framework that can be used in the design of optimized imaging hardware with embedded algorithms. The project is closely related to the currently popular method of super-resolution using a single image and in the context of deep learning of artificial intelligence.

Download here: Optimized Sampling Approaches for Compressive Sensing

Benjamin Rise

Collins Aerospace 

‘Distributed Machine Learning for Automatic Annotation and Analyses of Vast Distributed Image Archives’

This PhD project is to tackle the challenge of efficiently analysing the content of vast volumes of high-resolution imagery with distributed machine learning techniques such as federated learning. The images are generated with emerging sensors and stored in locations that span the Earth. Were it possible to bring all the imagery to one central location, it would be possible to use centralised machine learning to auto-annotate the imagery and thereby generate a list of geo-temporally localised objects of each of many types.

Download here: Distributed Machine Learning for Automatic Annotation

Will Pearson


Sivananthan Laboratories 

‘Implementing Advanced Image Analytics in a Compressed Sensing and Machine Learning Framework’

This PhD will investigate generalised advanced analysis techniques to advance the capabilities (S)TEM imaging. Where applicable, supervised machine learning methods such as ANN, CNNs and Adversarial networks can be investigated for their ability to analyse images with varying degrees of noise within a microscopic setting. The aim of this project will be to leverage the support provided by the CDT to develop a generalised machine learning model capable of analysing hundreds of images.

Download here: Implementing Advanced Image Analytics

Jianyang Xie


Remark AI 

‘Development of Advanced AI techniques for Human Action and Behaviour Recognition’

This project will focus on the development of machine learning and high-performance computing methods for the accurate and effective recognition of human action and behaviour. Deep learning techniques will be investigated towards high recognition performance with smaller network architecture. New computing approaches will be studied to speed up the process via GPU. The models will be developed and validated by public and private datasets.

Download here: Development of Advanced AI techniques for Human Action and Behaviour Recognition


Cohort 4

PhD StudentProject PartnerProject Title and Description



'Faster Uncertainty Quantification of Hydrocodes'

The aim of this project is to develop a single, integrated approach to analysing and speeding up uncertainty quantification on complex systems. The specific system used here is hydrocodes, which are high-fidelity simulations of fluid dynamics, involving computationally expensive calculations pertaining to chemistry and physics.

Download here:  Faster Uncertainty Quantification of Hydrocodes

John Bentas Draken

'Bayesian Optimisation for Ever-Improving Immersive Emulation of Engagement with Fighter Jets.'

Draken Europe needs to ensure that the "red air" component of their training with the Royal Navy, RAF, and NATO is realistic and appropriate. This project will use machine learning to understand similarities between historic training exercises and propose new scenarios. Data from simulations will be augmented with synthetic data, and AI techniques like Bayesian Optimization will be used to recommend which simulations or real-world exercises to run next. The project will focus on distributed computational resources to process large datasets and develop models for articulating similarity between engagements.

Download here: Bayesian Optimisation for Ever-Improving Immersive Emulation

Trilitech 'Algorithms and Mechanisms on Blockchain.'

This project is focused on studying and designing mechanisms on decentralized settings and particularly on blockchains. The field of (Algorithmic) Mechanism Design has been well studied for many years. Blockchain is a distributed ledger that stores records of peer-to-peer transactions (and not only with the advent of smart contracts), with the underlying goal of being decentralised and publicly accessible.

Download here: Algorithms and Mechanisms on Blockchain

Bettina Hanlon GCHQ

'Learning Transparent Models from Data-driven Algorithms to Enhance Streaming Data Analysis'

The focus of the project is to enhance algorithms for streaming data. In a world that is becoming more and more data orientated, vast amounts of data arrive in continuous streams. Prior knowledge about phenomena that give rise to this data is desirable, as this can be used to train algorithms to more effectively process future data in real time.

Download here: Learning Transparent Models from Data-driven Algorithms


'Developing Efficient Numerical Algorithms Using Fast Bayesian Random Forests.'

Decision Trees are used in data science to estimate parameter values to classify data sets in terms of regression and discrete labels. Current attempts to estimate the parameters use methods like Bayesian Additive Regression Trees (BART) and Classification Additive Regression Trees (CART), relying on Bayesian models, such as Markov Chain Monte Carlo (MCMC), which are arguably less sophisticated than what can be achieved by using Random Forests. MCMC, despite having generalised variants such as No-U-Turn-Samplers (NUTS), is hard to use in applications where the number of parameters is often unknown, making it unusable for looking at trees where the number of nodes, branches and thus parameters changes every iteration.

Download here: Developing Efficient Numerical Algorithms Using Fast Bayesian Random Forests

Nova Systems

'Using Machine learning to train a digital test pilot for missions in turbulent environments.'

This project focuses on modelling and simulation activities for helicopter to ship operations. The objective is to develop a mathematical model of a human test pilot operating a helicopter to a ship. This digital test pilot will be trained using data gathered during real-world sea trials. It could then be used to conduct multiple virtual deck landings in order to establish the likely boundaries of a helicopter safe operational envelope. The exploitation of real-world test data investigated has the potential to enable exploration of a wider operational envelope and reduce cost and risks involved in real-world flight trials. 

Download here: Using Machine learning to train a digital test pilot

Aleph Insights

'Reinforcement Learning for Physically Aware Cyber Defence.'

This PhD project is trying to apply reinforcement learning algorithms in cyber defence scenarios while taking into consideration the impact it has on the real world. Currently, most decisions made in the context of cyber defence is to maximise availability of services on a network, as the assumption is this would increase the ability for the network to be used in such a way that it has some real-world effect.

Download here: Reinforcement Learning for Physically Aware Cyber Defence


'Algorithms and Decision-Making Processes in Distributed Attacker-Defender Games'

The project will consider a few missions from the air combat domain and map these to simpler/abstracted ‘canonical’ problems; these will form the focus for the early research. Ideally, the research will yield methods/algorithms that can be usefully mapped across to illustratively complex air-combat situations; later research will then focus on investigating these within a suitable configured game-derived simulation. 

Download here: Algorithms and Decision-Making Processes in Distributed Attacker-Defender Games


'Exploring Efficient Automated Design Choices for Robust Machine Learning Algorithms.'

This project is relates to applying Machine Learning (ML) tht currently requires the data scientist to make design choices. Different design choices can alter both how many hyper-parameters (e.g. neuron weights or kernel widths and cross-covariance terms) need to be considered but also how challenging it is to optimise the hyper-parameters of the ML algorithm. Since practitioners have limited time to perform sensitivity analyses with respect to these parameters, design choices are typically based on estimated performance with very limited consideration for the variance in this estimate. Robust performance requires that we do not optimise the hyper-parameters (e.g. using stochastic gradient descent) but generate a set of samples for the hyper-parameters that are consistent with the data and then average across these sampled values for the hyperparameters. 

Download here: Exploring Efficient Automated Design Choices


'Maximising Detection Performance Using High Performance Processing of Multi-Sensor Data.'

This project will develop state-of-the-art in signal processing algorithms to be used in submarine warfare.  Future sensing of submarines will involve robotic submarines and surface-ships as well as autonomous sensors deployed on the seabed.  To avoid the potential to give away the position of the sensors and for the ability of a submarine to avoid detection, passive listening signals using an array of hydrophones is preferable to active sensing. 

Download here: Maximising Detection Performance using High Performance Processing

Ultra Electronics

'Using machine learning and artificial intelligence to improve the tracking of vessels in sonar spectrograms.'

This PhD project explores creating an AI model that can correctly classify quiet targets in waterfall (sonar) data. Currently, waterfall data is analysed by human operators; however, this is time-consuming and expensive; these human operators outperform traditional automated passive contact follower algorithms, such as the Kalman and Alpha-Beta filters: these filters are susceptible to the abundant underwater noise and struggle with crossing tracks and quiet contacts. In contrast, humans can use their experience to learn how to mitigate the challenging aspects of the task. An automatic detection and tracking model that is more accurate and robust than traditional methods would reduce the human operator’s workload.

Download here: Using machine learning and AI to improve the tracking of vessels


Cohort 5

PhD StudentProject PartnerProject Title and Description



'Applications of Infinite Dimensional Compressive Sensing to Multi-Dimensional Analog Images using Machine Learning to Enhance Results'

The aim of my project is to develop a STEM imaging system based on infinite-dimensional compressive sensing that optimises a sampling strategy involving a continuous probe position domain as opposed to the current finite methods where the locations of the probe are priori fixed while the recovery algorithm maps subsampled data to an analogue image with low computational complexity. The outcome should take the form of a new machine learning backed sampling, and reconstruction algorithm that greatly improves the accuracy and speed over current STEM imaging techniques.

Download here:  Applications of Infinite Dimensional Compressive SensingApplications of Infinite Dimensional Compressive Sensing


The Dog's Trust

'Developing AI Methods for Animal Health and Welfare Monitoring'

Within the animal welfare field, large amounts of free text data are collected in the form of veterinary clinical notes and free text responses from citizen science surveys. This project aims to improve access too and insight from these data sources by developing language models optimized for the language used in these sources.

Download here: Developing AI Methods for Animal Health and Welfare Monitoring

Christian Pollitt


'Machine Learning of Behavioural Models for Improved Multi-Sensor Fusion'

The project considers sensors such as radar, electro-optics, IR, maritime, GPS etc and sensor models characterizing error statistics of detections or raw measurements. There are models with different levels of fidelity that capture the characteristics of these sensors and their false alarm statistics. Motion models are essential components of sensor fusion algorithms. Common types of motion models used in tracking and detection are Constant motion/turn, Particle and Kalman filters as well Dynamic Bayesian networks or Neural network-based models.

Download here: Machine Learning of Behavioural Models for Improved Multi-Sensor Fusion

Daniel Chadwick Raytheon 'Parallel Processing for Novel Navigation.'

This project focuses on exploring the power of GPUs and multi-threading CPUs to enhance particle filters for use in inertial navigation systems. GPS jamming technology is becoming more sophisticated and therefore there is a growing need for novel navigation solutions. Inertial navigation systems are currently too sensitive to acceleration measurement errors, which can allow drift in the position measurement, rendering the position inaccurate over long durations of time. The use of a particle filter offers a promising solution to mitigate these challenges and enhance the robustness of inertial navigation systems.

Download here: Parallel Processing for Novel Navigation

Daniel Nash Airbus

'Learning Transparent Models from Data-driven Algorithms to Enhance Streaming Data Analysis'

The main focuses of the project include the further development of a state-of-the-art CFD code and simulation of aircraft wing aerodynamics on high-performance computing systems necessitated by the need to generate the required data for modelling the intricate transonic aerodynamic phenomena. Also, the critical assessment of the latest CFD technology on suitable use cases in collaboration with the industrial partner’s domain experts to foster the acceptance and integration into end-user processes, while challenging the current industrial practice. Finally, the exploration of Machine Learning algorithms to derive practical tools for wing design problems that can incorporate data from disparate sources.

Download here: 


'Coming soon'

Finley Boulton Dstl

'Machine Learning for Data Driven Sound Propagation Modelling'

This project will develop a series of high-fidelity digital twins capable of encapsulating a number of critical dynamic phenomena, which affect the propagation of sound waves through ocean environments, including internal waves, multi-scale structural thermal and temporal variations and fluctuations, scattering by non-smooth interfaces and boundaries (e.g. semi-submerged structures, sea bed, surface), currents, eddies, and fronts. 

Download here: Machine Learning for Data Driven Sound Propagation Modelling

Finn Henman Dstl

'Machine Learning Inference of the Ocean Environment from Acoustic Data'

The focus of the project is on machine learning models that can use acoustic data collected by in-situ sensors and remote sensors, modelled data, historical data, and data from other sources, to infer acoustically relevant properties of the ocean environment, from which to build an up-to-date and accurate representation of the acoustic environment for any sonar deployment.

Download here: Machine Learning Inference of the Ocean Environment from Acoustic Data

Omree Naim Hensoldt

'Developing Novel Bayesian Track Before Detect Approaches for Maritime Big Data Challenges'

This PhD investigates ways in which novel Bayesian algorithms can be used to improve track before detect (TkBD) for use in maritime radars, by taking the data and extracting from this bathymetric data on waves and other surface clutter as to distinguish low-observable surface objects (e.g. periscopes and USVs) from and within the clutter to be detected and tracked. This project will have a heavy focus on signal processing algorithms and such Bayesian Algorithms as Particle Filters. 

Download here: Developing Novel Bayesian Track Before Detect Approaches

Richard Jinschek SenseAI

'Constructing a Digital Twin for a self-correcting Scanning Transmission Electron Microscope using Machine Learning Approaches'

The goal of this PhD project is to develop a full digital twin model for the Scanning Transmission Electron Microscope (STEM) to enable in silico optimisation of experiments, reconstruction algorithms and the adaptive sub-sampling approach needed to generate unique scientific insights across the length and timescales of the instrument function (microns to atoms and picoseconds to hours). Included in this development will be statistical emulation using a multi-output Gaussian Process (MOGP), which enables machine learning algorithms to consider uncertainty across multiple signals, which in this case will be the many correlated images/spectra generated by a single experiment in the microscope. From this development it is anticipated that image optimisations such as focus, tilt, stigmation and aberrations can be self-compensated for during acquisition, taking the key first steps to self-driving experimental approaches to materials characterisation. 

Download here: Constructing a Digital Twin

Ruojun Zhang

The Clatterbridge Cancer Centre

'Using Artificial Intelligence to Help Predict Treatment Response in Patients.'

The project aims to advance the field by creating and assessing novel AI tools that can predict cancer patients' response to treatments with unprecedented accuracy. This initiative holds the promise of providing doctors with valuable guidance on the most effective treatments tailored to individual cancer patients. Intersecting various disciplines, the project will leverage extensive multi-modal data encompassing image data, clinical symptoms, and demographic information, all sourced from the clinical partners. These diverse data sets will serve as the building blocks for the innovative foundation AI model. 

Download here: Using Artificial Intelligence to Help Predict Treatment Response in Patients

Teodor Avram Ciochirca Raytheon

'Machine Learning for Bio-Inspired Navigation'

This project is focused on leveraging machine learning towards enabling navigation in GPS-denied environments, by developing novel navigation systems. Biologically inspired, the novel navigation system will mimic the migratory mechanisms of animals over long distances, by fusing a multitude of animal sensors.

Download here: Machine Learning for Bio-Inspired Navigation

'Coming soon'

Wenping Jiang Newson

'Developing Reinforcement Learning and Artificial Intelligence Tools to Support Clinical Care Including Care for Women with Perimenopausal and Menopausal Symptoms'

We aim to develop machine learning methods to better interpret and understand Newson Health’s internal clinical data, which can provide clinically valuable insights and has the potential to improve patient care. We also aim to develop deep reinforcement learning methods to automatically learn optimal individualised treatment regimens from clinical data. We will explore the combination of supervised/unsupervised and reinforcement learning to leverage previously collected clinical data to aid the online learning process. Offline reinforcement learning and Generative AI tools will also likely play an important role in our development process. Our research has the potential to improve treatment outcomes and personalised care.

Download here: Developing Reinforcement Learning and Artificial Intelligence Tools NH

Meet our Future Leaders in Distributed Algorithms here.