University of Warwick, 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 University of Warwick, England
Eligible candidate may Apply as soon as possible.
(01) PhD Degree – Fully Funded
PhD position summary/title: PhD in Modelling and Optimisation of Battery Electrode Slurry Preparation Process by Extrusion
Lithium-ion battery and its alternatives continue to advance to meet the ever-growing need for energy storage, and electric transportation systems. With increased demand for electric vehicles, e-aircrafts, e-bike, etc, and the environmental imperative to harness clean energy, battery production and development is more important than ever before, and battery manufacturers need optimised process technologies to ensure quality and efficiency in their operations.
Mixing is the first controllable process in battery manufacturing, where the characteristics of the slurry and the associated coated electrodes could be defined and optimised. Compared to the batch mixing, extrusion mixing technique has the ability to mix electrode active material with binders and additives while using significantly less solvent which reduces the environmental impact of the solvent-based electrodes and facilitates continues mixing practice and quality assurance.
The problem is that the relation between the extrusion process factors (e.g. feed rate, temperature, pressure) on the slurry, electrode and finished cell characteristics is complex and compare to extrusion in other industries is quite new for batteries with electrochemical components. Therefore, the research questions of this PhD is: “What is the impact of the extrusion mixing process key factors in combination with the equipment structure (e.g. screw configuration, dimensions) on the electrochemical, mechanical and structural characteristics of the slurry, electrodes, and final battery cells?”
Deadline : Open until filled
(02) PhD Degree – Fully Funded
PhD position summary/title: Thermal route optimization of predictive controls to improve BEV efficiency using AI & ML
Route information has significantly improved the optimization of hybrid vehicle propulsion by determining the most efficient power source for different parts of a journey. It’s commonly used for eco-coaching by influencing driving behaviour for better fuel efficiency. However, the potential for leveraging route data to optimize energy consumption in Battery Electric Vehicles (BEVs) has been less explored. This project introduces an innovative approach to enhance BEV Thermal Management using route-specific data, incorporating factors like vehicle speed, V2X, traffic, and weather details.
This project aims to address the following challenges:
- Utilizing Route Information & e-Horizon Integration: Exploring methods to optimize thermal management system (improving range, efficiency, and passenger comfort).
- Applying Artificial Intelligence & Machine Learning: Investigating the use of AI and ML techniques to learn and adapt optimal settings for thermal management control systems based on varying route conditions.
- Implementing Hierarchical Control: Developing and implementing hierarchical control strategies for multi-level thermal management systems to effectively regulate temperature and energy usage.
Deadline : Open until filled
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(03) PhD Degree – Fully Funded
PhD position summary/title: PhD in 3D-printed proprioceptive materials with integrated active feedback systems for real-time shape optimisation
This PhD research proposal aims to design, fabricate, and characterise proprioceptive materials integrated with active feedback systems for real-time shape optimisation in defence applications. Using 3D printing technologies, the project focuses on the development of a specific device featuring shape memory alloys and electroactive polymers as proprioceptive materials. These materials will be printed with integrated sensors to form a cohesive, structurally robust system.
The process of shape optimisation will be implemented through a highly integrated, closed-loop feedback system within the material structure, utilising advanced algorithms to interpret sensor data and adapt the material’s shape accordingly. The integration of sensors, actuators, and microcontrollers within, e.g. the PVDF matrix, using precise spatial arrangement provided by AM would provide unparalleled control over the material’s mechanical and functional properties, thus facilitating the creation of complex geometries and multi-functional structures with specific behavioural traits.
Deadline : Open until filled
(04) PhD Degree – Fully Funded
PhD position summary/title: Thermal route optimization of predictive controls to improve BEV efficiency using AI & ML
Route information has significantly improved the optimization of hybrid vehicle propulsion by determining the most efficient power source for different parts of a journey. It’s commonly used for eco-coaching by influencing driving behaviour for better fuel efficiency. However, the potential for leveraging route data to optimize energy consumption in Battery Electric Vehicles (BEVs) has been less explored. This project introduces an innovative approach to enhance BEV Thermal Management using route-specific data, incorporating factors like vehicle speed, V2X, traffic, and weather details.
This project aims to address the following challenges:
- Utilizing Route Information & e-Horizon Integration: Exploring methods to optimize thermal management system (improving range, efficiency, and passenger comfort).
- Applying Artificial Intelligence & Machine Learning: Investigating the use of AI and ML techniques to learn and adapt optimal settings for thermal management control systems based on varying route conditions.
- Implementing Hierarchical Control: Developing and implementing hierarchical control strategies for multi-level thermal management systems to effectively regulate temperature and energy usage.
Deadline : Open until filled
(05) PhD Degree – Fully Funded
PhD position summary/title: PhD in Advanced Battery Design for Future Electric Vehicles
Significant advances have been made understanding the performance of lithium-ion batteries. However, less consideration has been given to the wider, multidisciplinary engineering challenges associated with battery design and manufacturability that will underpin the successful design of new battery systems for future electric vehicles (EVs) and aircraft. Meeting future EV requirements mandates a fundamental revaluation of how batteries are designed. WMG and Jaguar Land Rover have identified that significant innovation opportunities exist around new battery concepts that improve performance and sustainability.
The aims of this PhD include:
- To create a clear vision for how future EV requirements (e.g., sustainability, performance, safety, cost) can be cascaded to support the optimisation of new battery concepts.
- To devise new methods to improve our understanding of battery expansion, heat dissipation and mechanical loading.
- To design new methods to increase product safety at the battery and vehicle scale.
Deadline : Open until filled
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(06) PhD Degree – Fully Funded
PhD position summary/title: PhD in Advanced Characterisation of Large Format Lithium-Ion Battery Failures
WMG and Jaguar Land Rover have been researching battery safety for over 10 years. This includes the creation of novel and repeatable methods of battery failure initialisation using laser technology and the integration of sensors within lithium-ion batteries to measure internal battery states such as core temperature, gas pressure and gas composition. Much of this research is often discussed using generic terms such as “battery thermal runaway” or “battery abuse testing”.
The primary aims of this PhD project are:
- To evaluate the feasibility of concurrently measuring internal battery temperature, gas pressure and gas composition within physically larger battery concepts appropriate for future electric vehicle integration.
- To explore the use of novel failure initialisation methods (e.g., lasers) to robustly and repeatably induce different battery failure modes beyond to those possible using conventional test methods.
Deadline : Open until filled
(07) PhD Degree – Fully Funded
PhD position summary/title: PhD in Advanced Battery Design for Future Electric Vehicles
Significant advances have been made understanding the performance of lithium-ion batteries. However, less consideration has been given to the wider, multidisciplinary engineering challenges associated with battery design and manufacturability that will underpin the successful design of new battery systems for future electric vehicles (EVs) and aircraft. Meeting future EV requirements mandates a fundamental revaluation of how batteries are designed. WMG and Jaguar Land Rover have identified that significant innovation opportunities exist around new battery concepts that improve performance and sustainability.
The aims of this PhD include:
- To create a clear vision for how future EV requirements (e.g., sustainability, performance, safety, cost) can be cascaded to support the optimisation of new battery concepts.
- To devise new methods to improve our understanding of battery expansion, heat dissipation and mechanical loading.
- To design new methods to increase product safety at the battery and vehicle scale
Deadline : Open until filled
(08) PhD Degree – Fully Funded
PhD position summary/title: PhD in Advanced Characterisation of Large Format Lithium-Ion Battery Failures
WMG and Jaguar Land Rover have been researching battery safety for over 10 years. This includes the creation of novel and repeatable methods of battery failure initialisation using laser technology and the integration of sensors within lithium-ion batteries to measure internal battery states such as core temperature, gas pressure and gas composition. Much of this research is often discussed using generic terms such as “battery thermal runaway” or “battery abuse testing”.
The primary aims of this PhD project are:
- To evaluate the feasibility of concurrently measuring internal battery temperature, gas pressure and gas composition within physically larger battery concepts appropriate for future electric vehicle integration.
- To explore the use of novel failure initialisation methods (e.g., lasers) to robustly and repeatably induce different battery failure modes beyond to those possible using conventional test methods.
Deadline : Open until filled
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(09) PhD Degree – Fully Funded
PhD position summary/title: PhD in Behaviours of Nitrogen in the Future Green Steelmaking Routes
An enthusiastic individual is being invited to join a team of researchers to work on the Warwick Industrial Fellowship funded project sponsored by Tata Steel in the Netherlands. By adopting hydrogen and renewable electricity based green steelmaking, Tata Steel in the Netherlands has committed to reducing its CO2 emissions with 35 – 40% by 2030 and being CO2-neutral by 2045. The aim of this project is to create fundamental knowledge of nitrogen behaviour under future green steelmaking scenarios to support steel industry decarbonisation.
Steel is an irreplaceable material in our modern life, while steel industry accounts for 9% of global anthropogenic CO2 emissions. A variety of low emission steel manufacturing processes are being developed to convert the currently dominating Blast Furnace – Basic Oxygen Furnace (BF-BOF) steelmaking route to low CO2 or CO2 free steelmaking route. However, one of the technical challenges for the new steelmaking routes is the achievable nitrogen content in the steel produced. Some high-quality steels demand good formability and toughness, along with good surface quality, which necessitates controlling nitrogen to very low levels of ~20-30 ppm. This is achieved by the current BF-BOF steelmaking route because of its excellent nitrogen removal capability. However, alternative metallic charges with low or no carbon content (carbon-free direct reduced iron, remelted direct reduced iron, steel scrap) are expected to adversely impact the thermodynamics and kinetics of N2 in the future green steelmaking routes.
Deadline : Open until filled
(10) PhD Degree – Fully Funded
PhD position summary/title: PhD in Behaviours of Nitrogen in the Future Green Steelmaking Routes
An enthusiastic individual is being invited to join a team of researchers to work on the Warwick Industrial Fellowship funded project sponsored by Tata Steel in the Netherlands. By adopting hydrogen and renewable electricity based green steelmaking, Tata Steel in the Netherlands has committed to reducing its CO2 emissions with 35 – 40% by 2030 and being CO2-neutral by 2045. The aim of this project is to create fundamental knowledge of nitrogen behaviour under future green steelmaking scenarios to support steel industry decarbonisation.
Steel is an irreplaceable material in our modern life, while steel industry accounts for 9% of global anthropogenic CO2 emissions. A variety of low emission steel manufacturing processes are being developed to convert the currently dominating Blast Furnace – Basic Oxygen Furnace (BF-BOF) steelmaking route to low CO2 or CO2 free steelmaking route. However, one of the technical challenges for the new steelmaking routes is the achievable nitrogen content in the steel produced. Some high-quality steels demand good formability and toughness, along with good surface quality, which necessitates controlling nitrogen to very low levels of ~20-30 ppm. This is achieved by the current BF-BOF steelmaking route because of its excellent nitrogen removal capability. However, alternative metallic charges with low or no carbon content (carbon-free direct reduced iron, remelted direct reduced iron, steel scrap) are expected to adversely impact the thermodynamics and kinetics of N2 in the future green steelmaking routes.
Deadline : Open until filled
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(11) PhD Degree – Fully Funded
PhD position summary/title: PhD in Verification and Validation of Safe Generative AI in Automative
As autonomous vehicles (AVs) transition from laboratories/test-tracks to public roads, ensuring their safety is paramount, as exemplified by the Cruise AV incident in October 2023. Utilising synthetic data to enable the training and virtual testing has been increasingly recognised as an effective practice for assuring AV safety. In addition to traditional simulators, Generative AI (GAI) is becoming a new way to generate synthetic data in the AV domain. However, how to ensure the responsible use of GAI for such safety-critical systems remains a key barrier, which is the question motivates this project.
In the scope of using GAI for training and testing AV perception components, we put forward the following research hypothesis: The effectiveness of GAI models in generating data for training and testing AV perception components hinges on adhering to key properties: robustness, explainability, fairness, privacy, and security. Each property must be clearly defined with measurable metrics and efficient estimation methods. For instance, robustness can be evaluated in terms of resilience to input variations, while explainability involves the model’s decision-making transparency. Upon accurately verify these properties, targeted improvement methods can be proposed to enhance the GAI model in these specific areas. To validate this approach, the creation of a benchmark and the conduct of case studies are crucial. These would serve as a standard for evaluating and refining GAI models, ensuring they meet ethical standards and contribute to the development of safer and more responsible AV technologies.
Deadline : Open until filled
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(12) PhD Degree – Fully Funded
PhD position summary/title: PhD in Tomography-based structural simulation for hybrid architecture carbon fibre composites
Carbon fibre-reinforced composites offer an exceptional strength-to-weight ratio, which makes them attractive for structural applications in many industries. However, fibre length is critical to leverage the strength characteristics of carbon fibres, and manufacturing complexity increases with fibre length. For example, continuous fibre composites provide exceptional mechanical properties, while discontinuous fibres provide manufacturing flexibility but with a penalty in strength. Hybrid architecture composites that combine continuous and discontinuous fibres in a single component have attracted growing interest in automotive and aerospace applications as they can provide a balance between mechanical properties and processability.
This project aims to develop an image analysis tool for quantifying fibre orientation and fibre content distributions from X-ray computed tomography (XCT) for hybrid architecture carbon fibre composites, and subsequently develop a model linking the material’s meso-structure to mechanical properties to create structural simulation models. XCT will be used to produce three-dimensional (3D) computer models of the imaged volume of a hybrid composite sample.
Deadline : Open until filled
(13) PhD Degree – Fully Funded
PhD position summary/title: PhD in Verification and Validation of Safe Generative AI in Automative
As autonomous vehicles (AVs) transition from laboratories/test-tracks to public roads, ensuring their safety is paramount, as exemplified by the Cruise AV incident in October 2023. Utilising synthetic data to enable the training and virtual testing has been increasingly recognised as an effective practice for assuring AV safety. In addition to traditional simulators, Generative AI (GAI) is becoming a new way to generate synthetic data in the AV domain. However, how to ensure the responsible use of GAI for such safety-critical systems remains a key barrier, which is the question motivates this project.
In the scope of using GAI for training and testing AV perception components, we put forward the following research hypothesis: The effectiveness of GAI models in generating data for training and testing AV perception components hinges on adhering to key properties: robustness, explainability, fairness, privacy, and security. Each property must be clearly defined with measurable metrics and efficient estimation methods. For instance, robustness can be evaluated in terms of resilience to input variations, while explainability involves the model’s decision-making transparency. Upon accurately verify these properties, targeted improvement methods can be proposed to enhance the GAI model in these specific areas. To validate this approach, the creation of a benchmark and the conduct of case studies are crucial. These would serve as a standard for evaluating and refining GAI models, ensuring they meet ethical standards and contribute to the development of safer and more responsible AV technologies.
Deadline : Open until filled
(14) PhD Degree – Fully Funded
PhD position summary/title: PhD in Plastics Analysis, Sorting & Recycling Technologies Through Intelligent Classification
Plastic remains an invaluable material for human society in many key areas, but the challenge of sustainable end-of-life disposal routes continues to exist. Accurate sorting and the production of high quality recyclate is a key target in order to ensure confidence and security in recycled plastic supply chains and subsequent manufacturing industries. This is essential for society to reach targets for the inclusion of recycled and recyclable content across sectors such as automotive and packaging. To meet this challenge, an infrastructure of knowledge-led and digitally-enabled systems that underpin future manufacturing needs to be developed.
Our previous work on AI & machine learning (ML) for sorting highlighted a need for practical solutions and we were the first to demonstrate the potential of deep learning methods to solve the sorting problem, followed up by demonstrating that using multiple data sources (e.g. IR, Raman and LIBS) can improve overall performance.
Deadline : Open until filled
(15) PhD Degree – Fully Funded
PhD position summary/title: PhD in Tomography-based structural simulation for hybrid architecture carbon fibre composites
Carbon fibre-reinforced composites offer an exceptional strength-to-weight ratio, which makes them attractive for structural applications in many industries. However, fibre length is critical to leverage the strength characteristics of carbon fibres, and manufacturing complexity increases with fibre length. For example, continuous fibre composites provide exceptional mechanical properties, while discontinuous fibres provide manufacturing flexibility but with a penalty in strength. Hybrid architecture composites that combine continuous and discontinuous fibres in a single component have attracted growing interest in automotive and aerospace applications as they can provide a balance between mechanical properties and processability.
This project aims to develop an image analysis tool for quantifying fibre orientation and fibre content distributions from X-ray computed tomography (XCT) for hybrid architecture carbon fibre composites, and subsequently develop a model linking the material’s meso-structure to mechanical properties to create structural simulation models. XCT will be used to produce three-dimensional (3D) computer models of the imaged volume of a hybrid composite sample.
Deadline : Open until filled
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(16) PhD Degree – Fully Funded
PhD position summary/title: PhD in Digital Solutions for Optimizing the Hydrogen Supply Chain
This research aims to address the significant challenges within the hydrogen supply chain by exploring its needs and developing digital supply chain solutions, including digital twins. Key areas of focus include understanding the requirements for hydrogen production, storage, transportation, distribution, and consumption across various applications such as industrial use, transportation, residential heating, and power generation. By analyzing these components, the research will identify critical supply chain challenges, resource availabilities, and opportunities to enhance efficiency and reduce costs while effectively meeting demand. The project will then develop digital solutions for supply chain optimization alongside creating digital twins for comprehensive modeling and simulation. These digital twins will enable detailed scenario analysis and predictive capabilities, providing insights into potential bottlenecks and enabling proactive management of the supply chain. The ultimate goal is to improve the efficiency, reliability, and sustainability of the hydrogen supply chain, fostering collaboration among stakeholders and paving the way for the broader adoption of hydrogen as a key energy source.
Deadline : Open until filled
(17) PhD Degree – Fully Funded
PhD position summary/title: PhD in Plastics Analysis, Sorting & Recycling Technologies Through Intelligent Classification
Plastic remains an invaluable material for human society in many key areas, but the challenge of sustainable end-of-life disposal routes continues to exist. Accurate sorting and the production of high quality recyclate is a key target in order to ensure confidence and security in recycled plastic supply chains and subsequent manufacturing industries. This is essential for society to reach targets for the inclusion of recycled and recyclable content across sectors such as automotive and packaging. To meet this challenge, an infrastructure of knowledge-led and digitally-enabled systems that underpin future manufacturing needs to be developed.
Our previous work on AI & machine learning (ML) for sorting highlighted a need for practical solutions and we were the first to demonstrate the potential of deep learning methods to solve the sorting problem, followed up by demonstrating that using multiple data sources (e.g. IR, Raman and LIBS) can improve overall performance.
Deadline : Open until filled
(18) PhD Degree – Fully Funded
PhD position summary/title: PhD in Strategies for Achieving Net Zero Supply Chain Emissions
Reducing and mitigating supply chain emissions presents a significant challenge for companies striving to meet net-zero targets. These emissions, also known as Scope 3 emissions, often constitute the bulk of a company’s carbon footprint and are predominantly beyond direct control. A CDP report highlights that supply chain emissions are, on average, 11.4 times higher than operational emissions, emphasising the urgent need for effective strategies to address these emissions. This PhD project explores the overarching question: “How can companies effectively reduce Scope 3 supply chain emissions through empirical strategies, enhanced collaboration, and increased transparency to achieve net-zero targets?” The primary goal of the research is to develop and validate data-driven strategies for mitigating supply chain emissions, fostering collaboration and transparency among supply chain partners, and enabling evidence-based decision-making to support companies in their pursuit of net-zero supply chain emissions.
Deadline : Open until filled
(19) PhD Degree – Fully Funded
PhD position summary/title: PhD in Digital Solutions for Optimizing the Hydrogen Supply Chain
This research aims to address the significant challenges within the hydrogen supply chain by exploring its needs and developing digital supply chain solutions, including digital twins. Key areas of focus include understanding the requirements for hydrogen production, storage, transportation, distribution, and consumption across various applications such as industrial use, transportation, residential heating, and power generation. By analyzing these components, the research will identify critical supply chain challenges, resource availabilities, and opportunities to enhance efficiency and reduce costs while effectively meeting demand. The project will then develop digital solutions for supply chain optimization alongside creating digital twins for comprehensive modeling and simulation. These digital twins will enable detailed scenario analysis and predictive capabilities, providing insights into potential bottlenecks and enabling proactive management of the supply chain. The ultimate goal is to improve the efficiency, reliability, and sustainability of the hydrogen supply chain, fostering collaboration among stakeholders and paving the way for the broader adoption of hydrogen as a key energy source.
Deadline : Open until filled
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(20) PhD Degree – Fully Funded
PhD position summary/title: PhD in Novel electrical steel development for high performing e-machines
E-machine manufacturing has experienced significant growth over the past five years, with projections indicating continued expansion. Improved efficiency can be achieved by design and materials optimization. Currently most e-machines utilize standard electrical steels (approx. 3 wt% Si grades) with materials choices for high-frequency motors being Fe-Co alloys (expensive but used for aerospace) and 6 wt% Si electrical steel (single supplier, expensive, non-sustainable production route). Therefore, there is an opportunity to develop improved electrical steels, produced using conventional mass production processing, for widespread use in e-machines that will directly enhance battery range but also facilitate the redesign of motors, resulting in further motor efficiency improvements.
At WMG, we have developed a new novel grade of electrical steel, based on conventional processing, with significantly superior properties (approx. 40% improvement is magnetic efficiency and 33% improvement in strength). This PhD will therefore focus on developing an understanding on composition-processing-property performance in a novel steel in the context of considering how this alloy could be manufactured using the current commercial processing route.
Deadline : Open until filled
(21) PhD Degree – Fully Funded
PhD position summary/title: PhD in Strategies for Achieving Net Zero Supply Chain Emissions
Reducing and mitigating supply chain emissions presents a significant challenge for companies striving to meet net-zero targets. These emissions, also known as Scope 3 emissions, often constitute the bulk of a company’s carbon footprint and are predominantly beyond direct control. A CDP report highlights that supply chain emissions are, on average, 11.4 times higher than operational emissions, emphasising the urgent need for effective strategies to address these emissions. This PhD project explores the overarching question: “How can companies effectively reduce Scope 3 supply chain emissions through empirical strategies, enhanced collaboration, and increased transparency to achieve net-zero targets?” The primary goal of the research is to develop and validate data-driven strategies for mitigating supply chain emissions, fostering collaboration and transparency among supply chain partners, and enabling evidence-based decision-making to support companies in their pursuit of net-zero supply chain emissions.
The expected outcomes of this research include empirically validated strategies and detailed mitigation plans that companies can adopt to reduce their supply chain emissions. Additionally, the project aims to develop a framework for enhancing collaboration and transparency among supply chain partners, which is crucial for effective emissions management. The research will also produce evidence-based tools and guidelines to aid sustainable supply chain management decision-making. Furthermore, a comprehensive sourcing strategy specifically focused on achieving net-zero emissions will be formulated. These outcomes will provide actionable insights for companies and contribute significantly to the academic body of knowledge on sustainable supply chain management.
Deadline : Open until filled
(22) PhD Degree – Fully Funded
PhD position summary/title: PhD in Developing high performance dynamic elastomer nanocomposites
EU regulation has placed an imperative on the development of alternatives for rubbers containing fluorine in critical applications, key requirements such as radiation resistance, low permeability, high and low temperature capability, have challenged the current elastomer technologies. This project aims to develop novel elastomer nanocomposites by exploring functional nanoparticles and new elastomer chemistry for high performance rubber sealing systems. The surface chemistry of nanoparticles and interface characterisation with elastomer will be studied, the structure-property-processing relationship of the rubber nanocomposites will be investigated through a suite of advanced technologies, such as atomic force microscopy, wide-angle X-ray scattering, rheology, and dynamic mechanochemical characterisation. The project will lead to high-performance rubber seal technology via industrial-compatible manufacturing processes. The aim is to develop new intellectual property that is ready for commercialisation. We are looking for a candidate who has educated in polymer science and technology, with sound experience in a range of practical laboratory skills. The ideal candidate should also be highly self-motivated and have solid skills and experience in polymer chemistry and processing.
This is an excellent opportunity to enhance your career within a research-intensive, and commercially focused environment. You will be based at the International Institute for Nanocomposites Manufacturing (IINM) and WMG, working with the Dr Chaoying Wan research team, with full access to the start-of-the-art polymer research facilities at WMG and Warwick WMG News Item (warwick.ac.uk). You will also work closely with the industry funder James Walker Ltd. and work closely with the industry teams. You will have support to further develop your skill base going forward.
Deadline : Open until filled
About The University of Warwick, England : Official website
The University of Warwick in post-nominal letters is a public research university on the outskirts of Coventry between the West Midlands and Warwickshire, England. It was founded in 1965 as part of a government initiative to expand higher education. Within the University, Warwick Business School was established in 1967, Warwick Law School was established in 1968, Warwick Manufacturing Group (now WMG) in 1980, and Warwick Medical School opened in 2000. Warwick incorporated Coventry College of Education in 1979 and Horticulture Research International in 2004.
Warwick is primarily based on a 290 ha (720 acres) campus on the outskirts of Coventry, with a satellite campus in Wellesbourne and a central London base at the Shard. It is organised into three faculties — Arts, Science Engineering and Medicine, and Social Sciences — within which there are 32 departments. As of 2019, Warwick has around 26,531 full-time students and 2,492 academic and research staff. It had a consolidated income of £688.6 million in 2017/18, of which £126.5 million was from research grants and contracts. Warwick Arts Centre, a multi-venue arts complex in the university’s main campus, is the largest venue of its kind in the UK outside London.{
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