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: SENSS DTP Student-led studentships
Deadline :22 Jan 2025
(02) PhD Degree – Fully Funded
PhD position summary/title: Optimise and automate pre-production for wire based Directed Energy Deposition (w-DEDAM) production PhD
Wire-based directed energy deposition additive manufacturing (w-DEDAM) systems have effectively constructed qualified parts, now extensively employed in many industrial applications. To ensure a stable, reliable, high-quality and environmentally sustainable deposition process, the pre-production process is crucial which includes multiple activities, in terms of pre-forming original Computer Aided Design (CAD) models, recognising and segmenting design features, simulating geometry and mechanical properties, defining build sequences, and planning paths with appropriate process parameters.
Currently, the entire pre-production process is heavily reliant on the expertise and experience of additive manufacturing (AM) engineers. The decisions have also been decided based on prior experience, which may result in various part quality, lead time, and the use of material. This current artificial process is also time-consuming and fraught with uncertainties, often prone to human errors during decision-making. Therefore, there is an urgent need to fully optimise and automate this pre-production process with the combination of expert knowledge and artificial intelligence (AI) driven digital tools.
This project aims to explore and discover a non-expert pre-production process for w-DEDAM which can be implemented automatically based on expert knowledge and AI-driven digital tools combined with multi-objective optimisation. It will routinely provide an optimal production solution in terms of productivity, minimal or no distortion and high quality.
The student will be based at the Welding and Additive Manufacturing Centre, known for its impactful research into advanced fusion-based processing/manufacturing methods and other relevant technologies. This project is closely linked to many ongoing academic and industry projects, ensuring the student will be part of a diverse and vibrant research community. Additionally, there will be opportunities to work with the Centre’s industrial partners, such as WAAM3D and WAAMMat.
Deadline : 12 Feb 2025
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(03) PhD Degree – Fully Funded
PhD position summary/title: Combinatorial Artificial Intelligence for Defence Applications PhD
‘First Wave AI’ is used to describe the rules/logic based AI used heavily in the 1990’s and 2000’s and still in wide use today. This involves ‘handcrafted’ expert systems, which are good at reasoning about narrowly defined problems, but poor at handling uncertainty and have no ability to learn or abstract/generalise. In that sense, these systems serve as complex functional approximators trained over an input-output data set.
‘Second Wave AI’ is the term used to describe the current glut of ‘machine learning’ style intelligence, where algorithms are used that allow a computer to process large data-sets and learn patterns and behaviours, thus allowing them to respond when the same patterns are seen in new data. This include ‘supervised learning’ approaches (such as Deep CNN’s) and ‘unsupervised learning’ approaches (such as reinforcement based learning and generative adversarial networks). Some of the main problems with Second Wave AI are ‘explainability’ and trust – as the machines learn, they are based upon statistical outcomes on large data sets, rather than human intuitive information. Another problem lies with the fragility of the systems, ‘illogical’ outcomes can sometimes be generated due to biases, gaps or pollution of the training sets. They typically lack the ability to generalise and to reason beyond what it has been trained over.
It is an emerging opinion that the next advances will be achieved through combinations of these alternate approaches. These may be loosely coupled (novel applications of existing techniques) or tightly coupled, which involves new ways of defining and developing these intelligences to combine both approaches. As such, recent advances on techniques such as Meta Learning, One-shot/Few-shot Learning and Distributed/Decentralized Federated Learning not only provide approaches to combine intelligence but also ensure computational tractability of exponentially growing and unbounded variable and instance sets. In addition, novel approaches such as Physics Informed/Guided Learning allows the learning models to capture the underlying physics/patterns and to generate physically consistent regression (or classification) which is applicable not only to the limited physical envelope of the data, but to a wider extend and thus generalise. Such approaches provide a balance between infinite extent models and limited extend data based on trust over particular sets, and naturally create explainable AI structures which can further be analysed from a verification and validation perspective.
Deadline : 05 Mar 2025
(04) PhD Degree – Fully Funded
PhD position summary/title: Design and implementation of a self-sealing specimen chamber to allow shock loading of hazardous materials
High velocity impacts on targets can generate shock waves which travel through the target material. The response of the material to such extreme impacts is of interest to many areas of engineering, in particular the defence sector. Gas guns are used to enable fine control over the shock loading of targets to study the material response. The goal of the research project is to enable the study of materials which cannot be currently studied in the UK.
Deadline : 06 Nov 2024
(05) 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.
This implies that mathematical modelling of such data is infeasible. The data-driven modelling approach could resolve this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling using learning algorithms as Extreme Learning Machine (ELM) is that training data should cover the entire domain of process parameters to achieve accurate generalization of the trained model to new process configurations. In practice, this might not be possible, that is the sample data could cover only some space, not entire space, of process parameters. Integrating prior knowledge into the learning could enable accurate generalization of the data-driven model even when the space of system parameters is only sampled sparsely.
Consequently, it will improve the performance of the learning. Integration of the prior knowledge of the system into the learning procedure will be quite challenging since the key enabler of its very powers is the universal approximation capabilities. Sampled data are generally noisy, outliers occur, and there always exist a risk of overfitting corrupted data. Therefore, the learned function may violate a constraint that is present in the ideal function, from which the training data sampled. This PhD study will address this research challenge.
Deadline : Open until filled
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(06) PhD Degree – Fully Funded
PhD position summary/title: Forensic Study of Chinese Porcelains
The aim of the project is to investigate the possibility that analysis can shed new light on the history, provenance and dating of certain periods of Chinese porcelain, with the implication that this can be used to develop a reliable and practical scientific test to assist in the identification of later copies. The plausibility of this leading to an industry standard test would be explored.
Deadline : 30 Oct 2024
(07) PhD Degree – Fully Funded
PhD position summary/title: Gene editing to improve salt tolerance in legumes – PhD
This project will employ advanced molecular genetics techniques to identify and manipulate key genes and pathways that confer salt tolerance in legumes, designing crops that can contribute to food security in vulnerable regions, while at the same time they enhance soil quality.
With the support of Azolla Biotech Ltd. and Cranfield University the student will use CRISPR-Cas9 gene editing and Agrobacterium-mediated transformation methods to genetically improve the resilience and yield of legume crops under saline conditions and test their performance under glasshouse conditions.
The outcome of this project will have a significant contribution in improving food security and diet quality in areas were salinized soils limit agricultural production and affect smallholder farmer livelihoods.
The student will have the opportunity to present their findings in relevant national and international scientific conferences and benefit from excellent training opportunities at Cranfield. They will also be able to get involved in MSc teaching activities, through the co-supervision of MSc student projects.
Apart from developing their skills in plant molecular genetics, bioinformatics and tissue culture, during the course of their PhD they will also develop useful transferable skills, such as project management, written and oral communication and student supervision skills.
Deadline : 11 Dec 2024
(08) PhD Degree – Fully Funded
PhD position summary/title: Hydrogen for future space transportation PhD
R2T2 is an integrated doctoral programme run across eight UK universities, which seeks to provide the opportunity to pursue a PhD in space propulsion technologies. The programme is fully funded, and a budget of approximately £50k will be available to support practical experiments, including hotfire, cold-flow, wind tunnel experiments, or similar, according to the specific project’s needs. Where hotfire is indicated, technical oversight will be put in place, and the use of the MachLab test site at Machrihanish Airbase will be offered, although not required. In addition, a practical and online training scheme will be put in place, designed to offer an introduction to the skills required in the space launch industry. These will likely include project management skills, propellant handling, gas/cryo safety, and many more. Parts of this training programme will be delivered at Westcott.
Hydrogen for future space transportation will be held at Cranfield University with the support of Pulsar Fusion and Newton Launch Systems. Due to its low molecular mass and high flame temperature, hydrogen/oxygen is the highest performing conventional fuel for chemical space propulsion. However, this small molecular mass carries significant engineering challenges when storing hydrogen for extended periods (particularly in the vacuum of space).
The project will investigate different alternatives of long-term storage (in particular cryo-compression) and the related delivery and combustion challenges of high-density hydrogen engines for refuellable planetary spacecrafts.
Deadline : 16 Oct 2024
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(09) PhD Degree – Fully Funded
PhD position summary/title: Multi-body Hypersonic Aerodynamics PhD
The development of UK sovereign hypersonic technologies and expertise are key considerations in the development of new air systems. A key challenge for some of envisaged air vehicle configurations and applications is the prediction and evaluation of multi-body aerodynamic characteristics and the impact on body trajectories. The overall aim of the research is to determine the aerodynamic interference conditions that are likely to adversely affect multibody separation at hypersonic speeds and will include both computational and experimental elements.
The experimental studies will investigate the multi-body aerodynamics of canonical configurations in the Cranfield hypersonic gun tunnel. The experiments will provide a range of flow visualisations and quantitative measurements for CFD validation. The computational research will include validation simulations of the experimental configurations as well as multi-body arrangements with coupled 6-DOF models over a range of hypersonic Mach numbers to predict the unsteady body trajectories and aerodynamic interactions. The computations will also explore Exascale-ready Discontinuous Galerkin/Finite Volume unstructured methods for hypersonic interactions using the Cranfield University high-order UCNS3D code.
The research is funded through EPSRC, UK MoD and Cranfield University. During the PhD programme the student is expected to participate in centralised training and cohort building activities which will be coordinated by the UK Hypersonic Technologies champion, based at the University of Oxford.
Deadline : 23 Oct 2024
(10) PhD Degree – Fully Funded
PhD position summary/title: Three-dimensional hybrid composites for repair and recycling PhD
High-performance composites are increasingly used in a range of endurance critical applications where maintenance and repair opportunities are limited and costly e.g., autonomous vehicles and offshore wind turbines. Operational wear and tear and isolated damage events, like impact, can resulting in significant losses in material performance. Furthermore, at end-of-life, it is often difficult to separate out the components to allow for recycling.
This project simultaneously addresses the challenges of autonomous repairability and end-of-life recyclability by using through-thickness reinforcement to embed large-diameter (>1 mm) metallic hybridising elements into composite laminates. These novel through-thickness elements will be used as a means of targeted heat introduction to activate re-processible polymer matrices including thermoplastic or vitrimer-based resins. This intrinsic self-healing negates the need for external heating apparatus like hot presses or ovens to melt the composite’s matrix. The academic knowledge gap exists in understanding the thermo-mechanical interaction between metallic TT-elements and composite constituents which is foundational and necessary for exploiting this technology for repair and recycling purposes.
The Composites and Advanced Materials Centre at Cranfield University has world-class facilities to support through-thickness reinforcement activity and functional/mechanical characterisation of materials including an electrical, thermal, and thermo-mechanical characterisation suite, pilot scale composites manufacturing equipment including a tufting robot, and z-pinning gantry as well as the mechanical testing lab in the School of Aerospace, Transport and Manufacturing. This project supports a Royal Academy of Engineering Research Fellowship project entitled Multifunctional z-direction hybridisation of composites (web link: https://www.cranfield.ac.uk/research-projects/multifunctional-z-direction-hybridisation-of-composites).
The activities in this project will cover manufacturing process development and manufacture of hybridised components, optimisation of material parameters through finite-element simulation, testing and validation of functionality and performance. This will represent an industrial paradigm shift in the use of through-thickness reinforcement in composites, expanding this potential to recycling and repair. Increased longevity and durability of the structures associated with the solution will affect a savings on maintenance, repair, and replacement.
This work will involve collaboration with the Royal Academy of Engineering Research Fellowship project partners including Prof Michele Meo at the University of Southampton (UoS), Laser Additive Solutions (LAS), and the National Composites Centre (NCC). The candidate will be expected to engage with these collaborators as well as participate in knowledge exchange through a series of visits to project partners for manufacturing and testing trials, with a high level of interaction expected with the University of Southampton. The candidate will be expected and encouraged to participate in at least two industry crucial conferences including the International Conference on Composite Materials.
This role will develop practical composites manufacturing and process/performance simulation skills with a particular emphasis on through-thickness reinforcement which is currently an area of skills development that is in high-demand across the UK composite’s base. The candidate will be encouraged to engage with the in-house or external training activities for research and transferrable skills and there is an is an expectation that the PhD candidate will assist in the mentoring of MSc students which will endow useful people management and learning support skills.
Deadline : 27 Nov 2024
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(11) PhD Degree – Fully Funded
PhD position summary/title: Sensing wastewater for real-time public health – PhD
The PhD studentship was funded by a five-year Leverhulme Research Leadership Award on sensing wastewater for real-time public health to support 3 postdoc positions and two PhD student. You will contribute to the delivery of Cranfield University’s research portfolio in the Advanced Sensors Laboratory for Water-Environment-Health led by Prof Zhugen Yang, preferably with a background in biosensing, microfluidics, analytical science, molecular biology, microbiology and engineering background.
The candidate will conduct research project and be trained with skills sets including biosensing, microfluidic, wastewater surveillance, signal amplification strategy, CRISPR/Cas assay, and biocomputing experiments using state-of-the-art facilities in the UKCRIC-funded advanced sensors laboratory at Cranfield and national and international collaborators from academics, industry and governmental sectors.
Cranfield University is number one in the UK for training and producing engineering and technology postgraduates. It is one of the top five research-intensive universities in the UK and has an unrivalled reputation for transforming cutting edge technology, management and science into practical, life-enhancing solutions. This research project will be conducted within the Water Science Institute, School of Water, Energy and Environment, in collaboration with national and international collaborators such as academics from MIT(USA), University of Queensland (Australia) and industry partners.
We have established an Advanced Sensors Laboratory funded by UKCRIC, and this dedicated laboratory is the centre of the world-class research into sensors and their uses in water and the water industry being conducted at Cranfield. With an impressive legacy in biosensors, Cranfield’s UKCRIC sensors lab continues the University’s work in this area by providing state-of-the-art facilities for chemical, biological and microbial sensors’ design, elaboration, characterisation and application. The advanced sensors group (led by Prof Zhugen Yang) aims to explore multiciliary approach to advance sensor technology and address global challenges in water-environment-health nexus. The projects underway involve aspects of diverse disciplines, ranging from synthetic biology, microfluidics, biosensing, chemistry, environmental, biomedical and analytical science to nanotechnology.
We have demonstrated in India and Uganda a DNA-based paper-origami device which exploits hot wax printing to integrate sample preparation and microfluidic flows for pathogens detection. These low-cost assays are both sensitive and specific for pathogen detection in drinking water. We also recently demonstrate our paper-based sensors for the field detection of COVID-19 in local quarantine hotel for early warning of infectious disease in the community, and this has been featured in Science and wide public media coverages and displayed in London Science Museum.
This project aims to further improve the performance of our device (in terms of multiplexing pathogens,) with new engineering approaches, and evidence their impact so that they can find widespread application in both rural and urban environments, e.g., using CRISPRS/Cas system and synthetic biological tools.
The student will be widely engaging with a multidisciplinary team to learn advanced sensor technology and interact with stakeholders (e.g., UKHSA, Water utilities etc) to disseminate the research output. The funding supports travel throughout the project to meet with the collaborators, along with opportunities to attend and present results at international conferences (e.g., Biosensors Congress). Cranfield University are leading a UK water and wastewater network, as well as involved water sensors network, involving academic, industrial and public sector organisations; It is expected that the PhD researcher will become involved in this network, enabling the researcher to develop their profile in the sector and engage with experts in related areas.
Deadline : 09 Oct 2024
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(12) PhD Degree – Fully Funded
PhD position summary/title: Developing ontologies for digital engineering and manufacturing PhD
Whilst data is becoming more available, we need better ways to manage its use. This is where ontologies offer an exciting opportunity, with its ability to to enable knowledge sharing and reuse by means of common data architectures. This approach will semantically enhance the data, which will enable interoperability across a system of systems environment.
Deadline : 09 Oct 2024
(13) PhD Degree – Fully Funded
PhD position summary/title: Aero-Engine Experimental Aerodynamics PhD
For future aircraft concepts there is an expectation that new architectures feature closer integration of the propulsion system with the airframe. A key challenge for such configurations will be the more complex aerodynamic characteristics associated with the propulsion system integration. Within that context there is an on-going interest is developing new flow measurement capabilities for new aero-engine configurations. Previous work within the group at Cranfield has demonstrated the use of stereo PIV methods for a range of aero-engine related topics such as intake ground vortex, distortion ingestion as well as the characterisation of unsteady flows for complex intake configurations. These have demonstrated the considerable benefits of PIV to provide rich flow field measurements which are unobtainable using conventional methods.
The overall aim of this PhD project is to determine the feasibility and potential benefits of using event based cameras for stereo PIV measurements and to characterise the dynamic flow distortion for internal domains such as convoluted intakes. The experiments will initially focus on relatively simple configurations to explore the measurement system capabilities and then develop to more complex configurations where the focus will be on flow distortion.
The main impact of the work will be through Rolls-Royce where it will enable improvements to the measurement capability for intake flow distortion. During the course of the studies, it is also expected that the student will undertake a placement with Rolls-Royce.
Deadline : 16 Oct 2024
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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