Fully funded Ph.D. opportunity in Aerospace AI. Sponsored by EPSRC and BAE Systems covering tuition, fees and a bursary of up to £19,569 (tax free) + £7,500 industrial top-up. Combinatory Artificial Intelligence (also known as Third Wave AI as initially described by DARPA) is the term that references the next foreseen advances within Artificial Intelligence. This stems from the two main styles of AI development over the last two decades. This research topic aims to define novel approaches to developing and combining these intelligences, utilizing both 1st and 2nd wave AI approaches, in the context of Defence applications.

Combinatory Artificial Intelligence (also known as Third Wave AI as initially described by DARPA) is the term that references the next foreseen advances within Artificial Intelligence. This stems from the two main styles of AI development over the last two decades.

'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/Decentralised 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.

This Ph.D. research aims to define novel approaches to developing and combining these intelligences, utilising both 1st and 2nd wave AI approaches, in the context of Defence applications. Such applications are expected to include: 

  • Robust and “functionally explainable” machine-aided decision support for Safety and Mission Critical objectives e.g. fault detection/tracing, evasive manoeuvring, target selection etc. 
  • Detailed semantic understanding of operational environments for Machine Situational Awareness, particularly within contested, congested and degraded scenarios.
  • Fully autonomous robust intelligence data processing to significantly reduce the reliance upon human analysts and counter huge increases in data volumes. 
  • Improved synthetic training utilising machine-based instructors, matched to individual training needs. 
  • Improved “Virtual Assistants” for the next generation of platform-operator interfaces.

The work is envisioned to have great impact on design and development of intelligent autonomous agents.

Fully funded PhD covering not only tuition, fees and bursary but opportunity to attend conferences and to link with industrial experts in the field.

The applicant is envisioned to further enhance and develop world class skills in AI and Machine Learning with application to hard and challenging defence problems providing a great skill set for employability after the degree in both industry but also academia as well.

This PhD is open to students from the UK, EU and NATO countries, that meet certain security conditions. The student will be working in sensitive topics and must be able to pass vetting and gain security clearance.

At a glance

  • Application deadline05 Mar 2025
  • Award type(s)PhD
  • Start date01 Jun 2025
  • Duration of award4 years
  • Reference numberSATM517

Supervisor

Professor Weisi Guo is the Director of the Smart Living Grand Challenge and Head of Human Machine Intelligence Group at Cranfield. He is also a Turing Fellow with The Alan Turing Institute. He has been PI on £6.5m and investigator on over £19m of research funding. He has published 130+ journal papers (total IF 710+) and 80+ IEEE/ACM conference papers, with over 5700+ citations (h-index 39). This includes a Nature, Nature communications, Nature Machine Intelligence, Nature Comp.Sci., a top 10% cited paper in PLOS ONE, and several cover issues in Royal Society and IEEE journals. He currently serves as editor on several IEEE & Royal Society journals, and is a Full Member of the EPSRC peer-review college, as well as reviewing for UKRI FLF, ESRC, MRC, Royal Society, and Leverhulme. His research has won several international awards, including IET Innovation in 2015 and Bell Labs Prize Finalist in 2014 and Semi-Finalist in 2016 and 2019.

Entry requirements

Applicants must have a B.Sc. in engineering or a related area and must either have or close to having a Master’s degree (must be completed by the time of the start of the iCASE Award). A demonstrated background in aerospace, autonomy and AI/ML would be a distinct advantage.

This PhD is open to students from the UK, EU and NATO countries, that meet certain security conditions. The student will be working in sensitive topics and must be able to pass vetting and gain security clearance.

Funding

This is a fully-funded opportunity.

Sponsored by EPSRC, BAE Systems and Â鶹´«Ã½AV, this iCASE studentship will provide a bursary of up to £19,237 + £7,500 top-up (tax free) plus home fees* for four years.

This PhD is open to students from the UK, EU and NATO countries, that meet certain security conditions. The student will be working in sensitive topics and must be able to pass vetting and gain security clearance.

Cranfield Doctoral Network

Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network. This network brings together both research students and staff, providing a platform for our researchers to share ideas and collaborate in a multi-disciplinary environment. It aims to encourage an effective and vibrant research culture, founded upon the diversity of activities and knowledge. A tailored programme of seminars and events, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking opportunities.

How to apply

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If you are eligible to apply for this studentship, please complete the

This PhD is open to students from the UK, EU and NATO countries, that meet certain security conditions. The student will be working in sensitive topics and must be able to pass vetting and gain security clearance.

This vacancy may be filled before the closing date so early application is strongly encouraged.