Cranfield 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 Cranfield University, England.
Eligible candidate may Apply as soon as possible.
(01) PhD Degree – Fully Funded
PhD position summary/title: Acoustic velocimetry in gas turbine inlets with a view to non-intrusive mass-flow PhD
The overall aim of this PhD is to explore novel measurement methods that can improve the assessment of aero-engine performance. The research will focus on the measurement of massflow and velocity profiles using non-intrusive acoustic velocimetry. The project will include the development of a test rig and instrumentation for acoustic flow measurements, sensitivity to intake operating conditions and the exploration of data analysis methods to improve the overall measurement system accuracy. It will also include complimentary computational studies to predict the intake aerodynamic characteristics and aid in the experiment design.
Deadline : 10 Sep 2025
(02) PhD Degree – Fully Funded
PhD position summary/title: Adaptive AI for Wind Control and Predictive Maintenance PhD
This fully funded PhD studentship, sponsored by the EPSRC Doctoral Landscape Awards (DLA) and RES Group, offers a bursary of £25,000 per annum, covering full tuition fees. The project focuses on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands-on experience with real-world SCADA data, industry collaboration with RES Group, and training in high-fidelity simulation environments (OpenFAST, Digital Twin technology). This opportunity is ideal for those interested in renewable energy, AI for energy systems, and wind turbine engineering.
Deadline : 01 Oct 2025
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(03) PhD Degree – Fully Funded
PhD position summary/title: Enhancing Water Treatment using Robotic Tools for Filter Maintenance PhD
Deadline : 03 Sep 2025
(04) PhD Degree – Fully Funded
PhD position summary/title: Evaluating efficacy of supercritical water oxidation to valorise organic waste to achieve net zero targets and mitigate pollution PhD
This fully-funded PhD research opportunity, supported by EPSRC and Kairos Carbon offers a competitive bursary of £22,000 per annum, covering full tuition fees. The project will focus on the chemical mechanisms within the thermochemical conversion of wet organic wastes using supercritical water oxidation (SCWO), and will suit applicants with a strong understanding of chemistry and/or chemical engineering.
Deadline : 05 Nov 2025
(05) PhD Degree – Fully Funded
PhD position summary/title: Experimental and Numerical Study of Parameters Affecting the Wake Dynamics of a Simplified Car: Effects of Roughness – Turbulence – Yaw Angle
Air quality has become a major concern for public health as it causes respiratory and cardiovascular diseases, cancer, diabetes, and premature deaths. Air pollutants can be gaseous or solid particles (also referred as particulate matter). The impact of pollutants not only depend on the emitted quantity but also on other factors such as the proximity to sources and the dispersion conditions. The automotive sector makes a significant contribution to local air pollution and exposes the population to dangerous levels of pollutants. To reduce pollutant emissions, the automotive sector focused on reducing fuel consumption by decreasing the vehicle drag. This approach reduced pollutant emissions generated by road vehicles. However, the levels of pollutant emissions need to be further reduced.
To properly understand the transport and dispersion mechanisms, a good knowledge of the flow region where the pollutants are emitted is needed, which happens to be the wake flow behind a vehicle. Recent studies have revealed a strong relationship between the flow structures and the pollutant concentrations in the wake of simplified vehicle models. Different wake flow features (such as vortices, shear layers, and the recirculation region) play a major role in the mixing and dispersion of particulate and gaseous pollutants. These studies were conducted in wind tunnels under ideal conditions (low inflow turbulence intensity, laminar boundary layer, stationary ground). On one hand, highly controlled test conditions are required to enhance the basic understanding of the wake flow phenomena. On the other hand, ideal test conditions are often unrealistic. Solutions have been developed to introduce realistic road conditions in wind tunnel testing such as the introduction of a rolling road to reproduce ground effects (roughness), yaw angle variations to consider realistic crosswind configuration, and inflow turbulence conditions. The aim of this PhD study is to further understand the governing aerodynamic processes in the wake flow of realistic car shapes and their implications on pollutant dispersion under actual road conditions.
Deadline : 03 Sep 2025
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(06) PhD Degree – Fully Funded
PhD position summary/title: Extreme learning to handle ‘Big Data’ PhD
As aerospace platforms go through their service life, gradual performance degradations and unwarranted system failures can occur. There is certain physical information known a priori in such aerospace platform operations. The main research hypothesis to be tested in this research is that it should be possible to significantly improve the performance of extreme learning and assure safe and reliable maintenance operation by integrating this prior knowledge into the learning mechanism.
The integrating should enable to guarantee certain properties of the learned functions, while keep leveraging the strength of the data-driven modelling. Most of, if not all, the traditional statistical methods are not suitable for big data due to their certain characteristics: heterogeneity, statistical biases, noise accumulations, spurious correlation, and incidental endogeneity. Therefore, big data demands new statistical thinking and methods. As data size increases, each feature and parameter also becomes highly correlated. Then, their relations get highly complicated too and hidden patterns of big data might not be possible to be captured by traditional modelling approaches.
Deadline : Open until filled
(07) PhD Degree – Fully Funded
PhD position summary/title: Future of enterprise level knowledge management: Developing ontologies for digital engineering and manufacturing
This PhD aims to develop a common ontology based methodology to address interoperability issues both in the context of complex assets. A breakdown of the objectives is provided below:
- Objective 1 is focusing on mapping the current data types, structures, and interoperability challenges to develop a detailed understanding of the current processes. as-is model.
- Objective 2 will aim to develop approaches to measure and assess interoperability so that we can understand the scale of the challenge, and the source of interoperability challenges.
- Objective 3 will aim to develop an approach that can address any interoperability challenges to meet the minimal required level of decisional requirements.
- Objective 4 will focus on testing the developed approach to evaluate whether interoperability can be managed through BAE Systems based use case. This will help to evaluate the developed processes, metrics and ontology framework. Tools such as Shapes Constraint Language SHACL, a reference model for interoperability (NCT3TA), and interoperability standards (e.g. HISO 100xx, ISO/IEC 11179-xx) will be employed for efficient and effective framework development. PhD applicants are not required to know each of these approaches, as suitable training will be made available.
Deadline : 17 Sep 2025
(08) PhD Degree – Fully Funded
PhD position summary/title: Performance of supercritical CO2 power generation systems PhD
Applications are invited for a PhD studentship in the Centre for Propulsion and Thermal Power Engineering, Faculty of Engineering and Applied Sciences, Cranfield University, in the area of performance simulation, analysis, and optimization of supercritical CO2 power generation systems.
Cranfield has developed a unique methodology and software to simulate and analyse the performance of gas turbine engines in the past half century. The research in this area at Cranfield will be a good starting point for the proposed project. The focus of the research is to extend the existing performance and simulation capabilities to supercritical CO2 (sCO2) power generation systems for power generation, oil and gas, or marine applications. The critical components of sCO2 power generation systems, such as heat exchangers, compressors, turbines, condensers, and the whole system integration will be simulated thermodynamically. Performance design optimization and advanced performance simulation methods will be investigated, and corresponding computer software will be developed. The research will contribute to the development of digital twin technologies for sCO2 power generation systems.
Deadline : 26 Nov 2025
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(09) PhD Degree – Fully Funded
PhD position summary/title: PhDs in Defence and Security: Leadership, Management and Governance
The Centre has delivered short training and long degree-bearing courses in over 100 countries. Students are typically employed in central government, defence industry and the Armed Forces, and their doctoral studies resonate with their professional activities, including, for example, defence engagement, conflict resolution, gender studies, humanitarian and relief operations, international conflict and security law, defence budgeting and public finance. PhD candidates are required to attend an initial one-week induction programme. Thereafter, regular progress reviews are undertaken to ensure students receive continuous advice and guidance. Cranfield is well-known for providing close and supportive supervision, and students will be allocated primary and associate supervisors to offer mentorship across the entire period of doctoral registration.
Deadline : Open until filled
(10) PhD Degree – Fully Funded
PhD position summary/title: The Modelling and Design of a Liquid Hydrogen Tank-Integrated Booster Pump for Future Low Emission Aircraft PhD
Hydrogen has the potential to play a crucial role in decarbonising aviation in the long term, and to bring a revolution in air transport comparable to that of electric vehicles in the automotive sector. Contrary to current kerosene aircraft, the hydrogen fuel can’t be simply stored within the existing primary structure of the wings but require dedicated tanks. The use of pumps could generate significant weight savings, and improve the overall efficiency of the Hydrogen system, and at the end, of the aircraft. Nevertheless, to deliver those expected benefits, it is absolutely necessary to understand very well the behaviour of these cryogenic hydrogen pumps, in order to master their integration into the hydrogen system. The primary objective of this research in collaboration with Airbus is to develop a comprehensive, multi-fidelity suite of liquid hydrogen (LH2) pump models to predict and analyze pump performance, stability, and its interaction with the broader fuel system architecture for a future hydrogen fuel cell powered aircraft.
Deadline : 03 Sep 2025
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(11) PhD Degree – Fully Funded
PhD position summary/title: Ubiquitous Cognitive Navigation with AI Based Systems PhD
This fully-funded PhD studentship, sponsored by the EPSRC Doctoral Landscape Awards (DLA), Cranfield University and Spirent Communications, offers a bursary of £24,000 per annum, covering full tuition fees. This PhD project in the area of autonomy, navigation and artificial intelligence, aims to advance the development of intelligent and resilient navigation systems for autonomous transport. While autonomy is becoming more integrated into modern mobility, the reliability of Position, Navigation and Timing (PNT) systems—especially in environments where GNSS signals are denied or degraded—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient operation of autonomous systems in complex, real-world conditions.
Deadline : 01 Oct 2025
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About Cranfield University, England – Official Website
Cranfield University is a British postgraduate-only public research university specialising in science, engineering, design, technology and management. Cranfield was founded as the College of Aeronautics (CoA) in 1946. Through the 1950s and 1960s, the development of aircraft research led to growth and diversification into other areas such as manufacturing and management, and in 1967, to the founding of the Cranfield School of Management. In 1969, the College of Aeronautics was renamed the Cranfield Institute of Technology, was incorporated by royal charter, gained degree awarding powers, and became a university. In 1993, it adopted its current name.
Cranfield University has two campuses: the main campus is at Cranfield, Bedfordshire, and the second is at the Defence Academy of the United Kingdom at Shrivenham, southwest Oxfordshire. The main campus is unique in the United Kingdom (and Europe) for having its own airport – Cranfield Airport – and its own aircraft, used for teaching and research.
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