Coventry University, England invites online Application for number of Fully Funded PhD Degree at various Departments. We are providing a list of Fully Funded PhD Programs available at Coventry University, England.
Eligible candidate may Apply as soon as possible.
(01) PhD Degree – Fully Funded
PhD position summary/title: Atlantic Stories, Colonial Legacies and the Bodleian Library, 1650-1800
In response, this project proposes a series of case studies from within the Bodleian’s collections 1650-1800, to highlight early modern Atlantic colonial legacies and curatorial practice. Research questions include: what can an archival object or set of objects tell us about colonial activity? How has information about these objects been represented in Bodleian finding aids, both historically and currently? How has that created absences or silences relating to the history of empire?
The researcher will benefit from a multi-disciplinary team of supervisors and advisors. It is anticipated that the student will follow a two-month internship at the Bodleian to produce a mini research project that will support the dissemination of the PhD research and enhance future employability.
Deadline : 15 Jan 2025
(02) PhD Degree – Fully Funded
PhD position summary/title: Developing a Dimensional Metrology Cyber-Physical system to support new product introduction
For the dimensional integrity of the product, this fast iteration means that components and assemblies being measured often become disconnected from the digital tolerance simulation models. In addition, data collection and storage are not matured enough so that physical measurements and digital simulations can be easily compared to identify root causes.
Connecting these systems and using tolerance simulations to guide manufacturing improvements as part of the new product problem solving process represent a significant opportunity for faster scaling of the production system at the right quality level.
The aim of this PhD project to develop a cyber-physical architect between tolerance simulation and their associated metrology measurements within the manufacturing process. It is expected that this project will support problem solving in established manufacturers and scale-ups to provide a quality maturation methodology during product introduction.
Deadline : 25 October 2024
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(03) PhD Degree – Fully Funded
PhD position summary/title: Federated Learning Techniques for Cross-Domain Wi-Fi Sensing in Healthcare
Federated learning (FL) is a machine learning technique that allows multiple devices to collaboratively train a global model while keeping their data local. This is particularly useful for cross-domain Wi-Fi sensing, where devices in different environments collect different types of data.
One of the key challenges in cross-domain Wi-Fi sensing is the heterogeneity of data. Data collected from different environments may have different distributions due to factors such as the number of devices, the size and shape of the environment, and the presence of obstacles. This can make it difficult to train a single model that can accurately perform Wi-Fi sensing tasks across all domains.
This PhD project will address this research challenge by allowing devices to train local models on their own data. The local models will then be aggregated to train a global model.
The potential PhD student working on federated learning for cross-domain Wi-Fi sensing is expected to work on a variety of challenging and interesting problems. Some specific research areas that could be explored include:
- Developing new FL techniques that are more efficient and effective for cross-domain Wi-Fi sensing.
- Designing FL-based systems for specific cross-domain Wi-Fi sensing applications, such as HAR, indoor localization, and smart home monitoring.
- Evaluating the performance of FL-based cross-domain Wi-Fi sensing systems in real-world settings.
- The PhD student will also have the opportunity to collaborate with other researchers in the field and to publish their work in top academic conferences and journals.
Deadline : 25 October 2024
(04) PhD Degree – Fully Funded
PhD position summary/title: Modular Crash Structures for Autonomous Lightweight Passenger Rail Vehicles
The aim of the PhD work is to examine the crash safety requirements for the usage scenarios and vehicle variants given and focus on the development of a systematically methodical approach for the design and optimisation of modular crash energy absorption structures and technologies with special regard to the vehicles in question. The exemplary implementation of the methodical approach will include design of appropriate structures, their integration into the structure of the vehicle, and simulation of the resulting compositions in all configurations and under all relevant crash scenarios.
Deadline : 25 October 2024
(05) PhD Degree – Fully Funded
PhD position summary/title: Evaluating Regional Specific Training Stimulus (RSTS) for people with COPD attending pulmonary rehabilitation
This PhD project aims to evaluate the proof of concept and feasibility of an alternative paradigm of exercise training for older adults with COPD, incorporating low-mass, high-repetition training – ‘regional specific training stimulus’ (RSTS) – into existing programmes. Using both quantitative and qualitative methodologies, this research has the potential to provide new directions for pulmonary rehabilitation programme design and exercise prescription for people with COPD.
The PhD researcher will work closely with the multi-disciplinary NHS clinical academic pulmonary rehabilitation team. They will have a number of opportunities for professional and personal development through Coventry University and external training, with a focus on the development of clinical academic skills within the areas of rehabilitation, clinical trials, and co-production.
Deadline : 25 October 2024
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(06) PhD Degree – Fully Funded
PhD position summary/title: Accessible human-machine interfaces in transport: designed for people with disabilities
Research shows that poor transport design, whether public or private, can create challenges for disabled people and their ability to make door to door journeys.
In order to overcome some of these barriers, national disability charity Motability, in collaboration with the National Transport Design Centre (an accessible facility) at Coventry University, are seeking to sponsor a doctoral candidate to undertake research in the area of accessible transport solutions.
In line with the National Disability Strategy (2021), this PhD is focused on developing and assessing human-machine interfaces to enhance the accessibility of transport-related user interfaces. The applications of this research work can be, but are not limited to:
- Facilitating the use of transport ticket booths for those with cognitive, vision or dexterity impairments
- Developing a framework for designing transportation apps to mitigate the inconsistencies across them
- Designing and assessing an in-car touchscreen for individuals with dexterity and fingertips sensitivity impairments.
The PhD is not limited to the aforementioned topics, and participants are encouraged to suggest their ideas if they are related to human-machine interfaces.
A state-of-the-art, driver in-the-loop simulator is available to conduct this PhD studentship. The car can be instrumented depending on the requirements of the research.
We welcome applications for this unique and exciting opportunity from candidates who have innovative research ideas that can help to provide solutions and make transport more accessible. The research could also look at future transport solutions and emerging technologies that are still to be defined.
Deadline : 25 October 2024
(07) PhD Degree – Fully Funded
PhD position summary/title: Accessible cars: designed for people with disabilities
Research shows that poor transport design, whether public or private, can create challenges for disabled people and their ability to make door to door journeys.
In order to overcome some of these barriers, national disability charity Motability, in collaboration with the National Transport Design Centre (an accessible facility) at Coventry University, are seeking to sponsor a doctoral candidate to undertake research in the area of accessible transport solutions.
This Ph.D aims to develop a method or specific tools facilitating individuals with a disability achieving, or retaining, a driving license. The following topics could be explored:
- developing a framework of training tailored to individuals with disabilities
- designing devices supporting the use of the controllers (steering wheel, gear box, pedals etc.) within a car
- assessing the effect of one or more of these measures on the accessibility of a driving license for this population.
A state-of-the-art, driver in-the-loop simulator is available to conduct this Ph.D studentship. The car can be instrumented depending on the requirements of the research.
We welcome applications for this unique and exciting opportunity from candidates who have innovative research ideas that can help to provide solutions and make transport more accessible. The research could also look at future transport solutions and emerging technologies that are yet to be defined.
Deadline : 25 October 2024
(08) PhD Degree – Fully Funded
PhD position summary/title: Trailblazers: The Early Career Researcher and PhD Candidate Partnering Scheme
The Trailblazer PhD studentships have been devised and developed by leading early-career researchers at Coventry University. The scheme provides successfully appointed doctoral researchers with an innovative and dynamic intellectual space in which to undertake transformative research, whilst being fully supported by a team of experienced supervisors.
Deadline : Open until filled
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(09) PhD Degree – Fully Funded
PhD position summary/title: Numerical Investigation of Rotating Magnetoconvection in Liquid Metals
This PhD project will focus on the modelling of electro-magnetic effects, in particular, clarify the possible role of Alfvén waves in planetary core convection.
The successful candidate will implement increasingly sophisticated magnetohydrodynamic (MHD) models accounting for Alfvén waves in our in-house code goldfish. The PhD researcher will then carry out numerical simulations to assess their performance against cutting-edge experiments and against the classical quasi-static MHD approximation that ignores waves.
This project offers a unique opportunity to contribute to the field of planetary science and to develop a world-leading expertise in geophysical and numerical fluid dynamics. The successful candidate will also be able to develop an extensive scientific network by working with the best experts in the field word-wide, through extensive collaboration across the USA and Europe thanks to the great flexibility offered by the ERC-guarantee.
Deadline : 25 October 2024
(10) PhD Degree – Fully Funded
PhD position summary/title: Development of Artificial Intelligence Solution for Prediction of Preterm Birth
Preterm birth is a major global health burden affecting up to 15 million pregnancies per annum. It confers considerable neonatal mortality within one month of birth and numerous health risks for surviving babies. The main device currently available for routine uterine contraction detection is the tocodynamometer, which suffers from several drawbacks and provides limited information for the prediction of premature birth.
Currently, there is no clinically acceptable and accurate prediction method in clinical use for preterm labour. The more comfortable technique, electrohysterograhy (EHG), offers an alternative for uterine contraction monitoring, which could be used to identify preterm labour. As a minimum it requires only a pair of sticky electrodes placed on the lower abdomen. EHG, however, has not been used in clinical practice due to the difficulty of interpreting the raw EHG signals and unsatisfactory preterm prediction accuracy for clinical use from current research studies.
This interdisciplinary project aims to develop an innovative Artificial Intelligence (AI) solution to help achieve the quick acceptance of EHG as a reliable clinical monitoring tool and for accurate prediction of preterm labour. The data will be used is from existing datasets (two online databases, two in-house databases). The measurable objectives include: 1) Develop advanced algorithm to extract clinically useful features from the EHG recordings; 2) Develop AI algorithm to predict term/preterm labour based on deep learning method; 3) Pre-clinical efficacy test to further investigate and optimise the AI algorithm.
We seek a highly-talented, motivated, and open-minded candidate, with background in biomedical/electronic engineering, computer science or a related discipline. Experience in analysis of electrophysiological signals, programming language (e.g., Matlab, Python) as well as signal processing and machine learning techniques is highly desirable. Ability to manage time and work to strict deadlines is required.
Deadline : 25 October 2024
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(11) PhD Degree – Fully Funded
PhD position summary/title: Integrated Sensing and Communications for Vehicular Networks
The integrated sensing and communications (ISAC) technology is expected to be one of the key enablers of the sixth generation (6G) of wireless networks. ISAC refers to the use of radio signals to detect and estimate characteristics of objects in the environment, with the network acting as a “sensor” to comprehend the physical world it operates within. Combining disjoint communication and sensing systems into one system brings numerous advantages covering multiple aspects, such as ubiquitous sensing and communication, reduced power consumption, smaller number of required antennas, less cabling requirements, lower cost, and higher spectral efficiency. This can be particularly beneficial for connected and automated vehicles (CAVs), which strongly rely on sensing and communication capabilities. However, the current ISAC solutions cannot cope with the unique challenges (e.g., high mobility and stringent safety requirements) associated with CAVs, making them subject to high levels of interference. Therefore, this proposal will develop advanced physical layer solutions (i.e., advanced signal processing) and radio resource management (RRM) functionalities (e.g., multi-user access and resource allocation) for ISAC in vehicular environments. The performance of the developed solutions will be evaluated through extensive link-level (i.e., physical layer) and system-level (i.e., RRM) simulations and benchmarked against existing ISAC solutions.
Deadline : 25 October 2024
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(12) PhD Degree – Fully Funded
PhD position summary/title: Using Extended Reality (XR) to improve team performance and enhance training for maintenance of future vehicles
The NTDC was launched in 2017 where we employ state-of-the-art visualisation and simulation techniques, and design tools to address future mobility challenges from bicycles, through autonomous cars to eVTOL aircraft. You will have the opportunity to work alongside our team of human factors experts, psychologists, designers and engineering experts who have led impactful AR / VR research. Including the development of customer experience AR / VR applications for the world’s first urban air transport hub, and the design and evaluation of novel AR displays for future commercial aviation cockpits. NTDC works closely with industry, presenting excellent opportunities for collaboration and the development of impactful research. See out LinkedIn pages at https://www.linkedin.com/company/ntdc/.
The UK needs to invest in technologies for electrification, meaning many employers and employees will need to upskill, reskill and new-skill to meet the demand of delivering the future vehicles of the electric revolution. New technologies and a skilled workforce are both essential to meet the associated challenges of this fundamental industrial shift. Correspondingly, the current proposal will investigate the efficacy of head-worn XR technology applications, designed with a user-centric design (UCD) approach, for improving team performance and training of electrification maintenance skills.
The research addresses a requirement for Dstl’s Future Workforce and Training Programme. Specifically, to identify, develop and test novel human systems integration (HMI) and UCD approaches, in order to enable pan-defence users to understand and develop robust and/or novel approaches to achieving superiority through its people capability.
Deadline : 25 October 2024
(13) PhD Degree – Fully Funded
PhD position summary/title: Enhancing Healthcare Applications Through Cost-effective AI Solutions
The objective of this project is as follows: 1) Develop advanced deep active learning (DAL) algorithm to address the challenges posed by conventional methods; 2) Combine DAL with domain adaptation to reduce the annotation cost even further; 3) apply these algorithms to two healthcare applications: Uterine Contraction Identification and Cuffless Blood Pressure Estimation. This research marks a significant stride towards cost-effective AI, presenting a practical and impactful approach to advancing healthcare through innovative machine learning methodologies.
We seek a highly-talented, motivated, and open-minded candidate who are proficient in deep learning and transfer learning methodologies, with a strong foundation in advanced mathematical concepts. A strong background in signal processing would be desirable.
Deadline : 25 October 2024
(14) PhD Degree – Fully Funded
PhD position summary/title: AI/ML-driven Resource Management Framework for 6G MEC-assisted Industrial IoT Networks
Industrial internet-of-things (IIoT) use cases (e.g., self-driving cars and Industry 4.0) have stringent requirements (e.g., low-latency and high-reliability) that are out of reach of legacy connectivity solutions (e.g., Wi-Fi and 4G). While the advanced 5G features (e.g., time-sensitive networking (TSN) and ultra-reliable low-latency communication (URLLC)) can meet some of these requirements, they fall short in supporting the most demanding use cases.
In this context, three technological enablers can collectively overcome the limits of 5G. First, multi-access edge computing (MEC) allows to move the compute and analytics closer to the data, which reduces latency, alleviate traffic load on transport/core networks, and helps achieve privacy-preserving, enabling the dynamic deployment of applications closer to the edge. Second, Open RAN enables the automated closed-loop optimisation of the RAN, which is currently not possible with MEC. Third, AI/ML can collect and capitalize on the massive amount of data at the edge to achieve an efficient management, automation, and optimization of resources, while maintaining integrity and even ownership. These enablers, albeit useful, are complex and not straightforward to combine. Therefore, this project aims at constructing an AI/ML-driven Resource Management Framework for MEC-assisted IIoT networks, where synergies between these technologies are achieved in the IIoT context.
Deadline : 25 October 2024
(15) PhD Degree – Fully Funded
PhD position summary/title: Co-creating a HealthTech evaluation approach that captures what matters most to people living with one or more long-term conditions
This research seeks to explore the balance of the data needed to ensure useful, valid and reliable measurement, whilst considering how to design and present measures that are usable and acceptable to participants.
The research will involve comparing and contrasting approaches commonly used in HealthTech evaluation in terms of their appropriateness and acceptability to participants. Co-creation will also be employed to define ‘what matters most’ to people with long-term conditions, their families and wider communities in order to supplement more traditional approaches to measuring patient outcomes and experiences.
Deadline :25 October 2024
(16) PhD Degree – Fully Funded
PhD position summary/title: Understanding and addressing the uptake of Rehabilitative, Assistive and Restorative Technologies by underserved communities
This project seeks to explore and understand variability in the acceptance and adoption of rehabilitation, assistive and restorative technologies. The research will explore individual barriers to the use of such technologies, and identify patient groups in which technology is particularly under-utilised. Working with representative participant groups and stakeholders, the research will go on to co-create an action plan and technology design requirements to improve uptake and acceptance of technologies in under-served communities.
The project is co-funded by the NIHR Devices for Dignity HRC who specialise in the development of innovative health technologies for people with long-term conditions and have a key national role in developing technologies.
Deadline : 25 October 2024
(17) PhD Degree – Fully Funded
PhD position summary/title: Predicting cognitive performance in ADHD with advanced deep learning and network analysis in functional neuroimaging (Deakin led)
Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder characterized by symptoms of inattention, hyperactivity and or impulsivity. The diagnosis is based on subjective report, and as yet there are no objective biomarkers used clinically. In addition to the core symptoms, individuals with ADHD often present with a profile of cognitive deficits, that has been argued to results from lapse of attention. Being able to identify the brain networks and neural signature of an attentional lapse, will not only further our understanding of the neurobiology, but could also be used to monitor and predict cognitive performance, offering potential target for intervention.
This project aims to explore whether EEG and/or resting state fMRI can provide a quantitative and effective approach to identify a neural signature of attention lapses during cognitive task. Additionally, we would like to evaluate whether an integration of network-level analysis and deep learning techniques would improve the prediction compared with traditional methods.
Deadline : 15 January 2025
(18) PhD Degree – Fully Funded
PhD position summary/title: People Centred Productivity as a Pathway to Achieving Timely Circular Economy Interventions
The ethos of circular economy presents a pathway many products and services can make significant strides towards Net zero, however the adaptation from current processes and products to those enabled by the circular economy is challenging as investment is market lead, and currently the market is limited.
The project looks to utilise an ethical combination of people and data to provide a transitional pathway to rapidly demonstrate and deliver future markets within the circular economy without the need for significant investment in either adapting complex design or capabilities through capex.
Utilising technologies included augmented reality, real time AI driven directed decision capabilities, lean manufacturing and mathematical modelling, this project will look to explore the potential for directed intervention within the recovery of materials and components in “end-of-life” goods. Developing the knowledge required for value intervention across recycling, reconditioning and materials manufacturing sectors.
The student will have the opportunity to interact with industrial collaborators including technology developers, recyclers and manufacturing organisations, offering the potential for exposure to wider implication of their research and for clear challenge direction and problem-solving requirements from key stakeholders in the delivery of circular economy solutions.
Deadline : 25 October 2024
(19) PhD Degree – Fully Funded
PhD position summary/title: Unravelling Epigenetic changes in Diabetic Cardiomyopathy: A Novel Approach Using Human iPSC-Derived Cardiac Organoids
This exciting fully funded PhD studentship offers an opportunity to explore the correlation between Type 2 Diabetes (T2D) and increased mortality from myocardial infarctions (MI) in 3D cell culture models. People living with T2D have a twofold risk of dying from heart disease, accounting for 30-50% of all deaths in this group (BHF). Key factors in this increased mortality post-MI are elevated long-chain fatty acids (LCFAs) and failure to adapt to hypoxia (Dodd et al 2018).
The project aims to create a cardiac organoid model to focus on the role of alpha-ketoglutarate dependent oxygenase in controlling hypoxic adaptation processes, including RNA/DNA/histone methylation. Understanding the control of these adaptations and the molecular mechanisms at play is crucial for developing novel therapeutics to increase post-MI survival.
Under the guidance of Dr Michael Dodd, Professor Helen Maddock (Coventry University), and Professor Faizel Osman (University Hospitals Coventry & Warwickshire (UHCW)), the project aims to generate a human induced pluripotent stem cell (hIPSC) cardiac organoid model of diabetic cardiomyopathy. The model will be used to study how hypoxia and diabetes alter the epigenetic profile of 3D hiPSC cardiac organoids. With UHCW this model will be validated using cardiac tissue from T2D and control patients.
Additional requirements
- Knowledge and experience in cardiovascular metabolism
- Knowledge in cardiovascular epigenetic modification
- Knowledge of type 2 diabetes
- Experience of mammalian cell culture techniques, including stem cells
Deadline : 25 October 2024
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(20) PhD Degree – Fully Funded
PhD position summary/title: Electric Drives System of High Speed Permanent Magnet Motor Control
The proposed project aims to address the existing challenges of high speed motor drive system, such as low position detection accuracy, low system stability, and large current harmonics. To achieve that, the project will involve extensive research, including a comprehensive analysis of various inverter/controller topologies and corresponding modulation strategies to determine the most suitable options for the target applications. Additionally, the project will address various motor control strategies to ensure optimal performance by increasing system efficiency and reliability while reducing system complexity.
The importance of this research lies in its potential to enhance the sensorless position detection accuracy, improve system stability, and suppress current harmonics for various applications. This project has the potential to significantly impact multiple industries, making it a critical area of research and development.
Deadline : 15 Jan 2025
(21) PhD Degree – Fully Funded
PhD position summary/title: Modelling of Timber and Hybrid Structures for Automotive Crash Analysis and Optimisation
The motivation of this PhD is to develop a robust simulation framework which reliably captures the main failure mechanisms in vehicle structures under the governing loading conditions.
The aim of the PhD is to define and verify a rigorous and robust Finite Element model, methodology and/or framework specifically for timber and hybrid timber material compositions for automotive crashworthiness analysis and optimisation. The model/methodology should capture the governing material failure mechanisms of wood as well as the influence of specific material properties e.g. the influence of wood density which is the main difference of different wood species.
Deadline : 25 October 2024
(22) PhD Degree – Fully Funded
PhD position summary/title: Investigating the Neurocognitive Mechanisms Underlying the Benefits of Classroom Exercise Breaks in School-Age Children
The present proposal aims to address the urgency of undercovering the underlying mechanisms by which CEB might improve cognitive performance in school-age children by using an interdisciplinary approach. The novelty of using a portable neuroimaging device (i.e., fNIRS) can be a significant advantage as it can be used in school settings compared to other traditional methods, often requiring lab visits (EEG/fMRI). Given this, we aim to advance the field by revealing underlying mechanisms of the ultimate benefit of CEB for children’s executive function.
Expected Outcomes:
1. Underlying Mechanisms: This project will explore the specific neural pathways through which CEB improves executive function. By using fNIRS, we can identify brain regions activated during CEB and how they might relate to improved executive function.
2. Evidence-Based Practice: This project aims to identify the optimal CEB characteristics (duration, intensity, exercise type) for maximising children’s cognitive benefits. By establishing these, we can promote wider adoption of CEB in schools, potentially leading to improved learning environments/academic performance.
3. Teacher Training/Resources: We will inform the development of teacher training materials/resources that emphasise the importance and benefits of incorporating CEB into daily classroom routines.
Deadline : 27 May 2025
(23) PhD Degree – Fully Funded
PhD position summary/title: Microbiology population modelling with the aid of computer algebra techniques
To study the spread of a disease or to stop an animal species from danger of extinction, one needs to develop and use population models. For example, the Allee effect, a population model where in addition to competition for resources species exhibit cooperative behaviour for survival, has been observed in honey bees, starfishes and bacteria to name a few examples. A single population with the Allee effect is well-studied in the literature.
However, connected populations with the Allee effect that have the possibility of migration is a current topic of research. There are some initial theoretical steps already taken where it has been shown that connected populations with the Allee effect can exhibit complex behaviour. Engineered E-coli bacteria can be used to study population models with more degrees of freedom for the experimentalist in comparison to doing the experiments on animals or plants. However, to guarantee that we do not miss any possible behaviour and outcomes of the model, the design of the experiment and the choice of parameter values need guidance from advanced computational techniques.
The student will start by reproducing an engineered E-coli bacteria exhibiting the Allee effect. Then the student will use computer algebra to predict all possible behaviour of the model. At this stage they should answer the following questions:
- Can the use of algebraic methods help inform a validation study of empirical results in the literature?
- Can this lead to the identification of issues in experimental design; or errors in computer algebra algorithms?
- Can this approach be used to inform empirical studies not yet undertaken, with suitable parameter values to maximise the diversity of the observed behaviour?
By uncovering more examples that exhibit complex behaviour using the guidance of the developed algebraic toolkit and observing them in the lab environment, the student validates the applicability of the tool in a general case and thus not only specific to the initial case study.
Deadline :25 October 2024
About The Coventry University, England – Official Website
Coventry University is a public research university in Coventry, England. The origins of Coventry University can be linked to the founding of the Coventry School of Design in 1843. It was known as Lanchester Polytechnic from 1970 until 1987, and then as Coventry Polytechnic until the Further and Higher Education Act 1992 afforded its university status that year and the name was changed to Coventry University.
Coventry is the larger of the two universities in the city, the other being the University of Warwick. It is the UK’s fastest growing university and the country’s fourth largest overall. It has two principal campuses: one in the centre of Coventry where the majority of its operations are located, and one in Central London which focuses on business and management courses. Coventry also governs their other higher education institutions CU Coventry, CU Scarborough and CU London, all of which market themselves as an “alternative to mainstream higher education”. Its four faculties, which are made up of schools and departments, run around 300 undergraduate and postgraduate courses. Across the university there are 11 research centres which specialise in different fields, from agroecology and peace studies to future of transport.
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