Academy funds leading engineers to tackle industry challenges

Seven Research Chairs and Senior Research Fellowships have been awarded to exceptional engineering researchers by the Royal Academy of Engineering, enabling them to address some of the biggest challenges faced by the engineering industry.

Covering a wide range of engineering disciplines and focusing on industry-relevant research, the projects include new tools for drug discovery; machine learning techniques to assist neurosurgeons; and systems to help autonomous vehicles “see” better.

The scheme aims to enhance the links between academia and businesses with each of the prestigious five-year positions co-sponsored by an industrial partner. The awardees are also expected to establish world-leading research groups in their field of engineering.

Two of the positions have been awarded through the UK government’s Investment in Research Talent initiative. The initiative recognises the crucial role of engineering research in the UK, with the government providing the Academy with a significant increase in funding over the next four years to help attract and retain the best engineering research talent.

The Research Chairs and Senior Research Fellowships appointments are as follows:

Dr Jacqueline Cole, University of Cambridge and STFC Rutherford Appleton Laboratory

BASF/ Royal Academy of Engineering Senior Research Fellow in Data-Driven Molecular Engineering of Functional Materials

Dr Cole is designing new materials and chemicals for use in solar cells, magnetic devices and catalysts. The current trial-and-error nature of material design is a major bottleneck to technological innovation. Using the latest advances in artificial intelligence, high-performance computing and database development, Dr Cole will take a data-centric approach to material discovery, using advanced algorithms to search extensive chemical datasets for potential new materials that will meet the exact requirements of a particular product.

Professor Amparo Galindo, Imperial College London

Lilly/ Royal Academy of Engineering Research Chair in Pharmaceutical Molecular Systems

Many failures in the development of new pharmaceuticals are attributed to poor solubility, which stops them being absorbed into the body. Professor Galindo and her group will develop new tools to model the behaviour of molecules in different environments, enabling researchers to predict the solubility of a drug compound and decide if it merits further development. These new tools are based on recent advances in multiscale modelling and state-of-the-art numerical methods, and have the potential to revolutionise drug discovery, design and manufacturing.

Professor Roy Kalawsky, Loughborough University

Airbus/ Royal Academy of Engineering Research Chair in Digital and Engineering Information Systems

Professor Kalawsky is developing next-generation digitalisation technologies, including modelling, simulation and visual analytics, to help accelerate aircraft design and production processes. His work with Airbus will include new engineering information systems, which will be combined with digital-twin and novel immersive technologies, such as augmented and virtual reality systems, to enable engineers to predict how an aircraft design will behave at every stage of its lifecycle. A key focus of the project will also be to improve the way users interact with complex data and information.

Professor Ian Kinloch, University of Manchester

Morgan Advanced Materials/ Royal Academy of Engineering Research Chair in Carbon Materials

Professor Kinloch’s collaboration with Morgan Advanced Materials will bring together fundamental research on carbon materials with traditional industrial knowledge to create a deep understanding of carbon-carbon composites. Using experimentation, state-of-the-art analysis and analytical models, the project will pave the way to new carbon pantographs and thermal insulators that use energy more efficiently. Carbon-carbon composites will also be combined with metals and ceramics for other applications in the automotive and aerospace industries.

Professor Tong Sun, City, University of London

Faiveley Brecknell Willis/ Royal Academy of Engineering Research Chair in Smart Railway Electrification: Evolvable from Contact to Contactless

Professor Sun is working with Faiveley Brecknell Willis to develop three new kinds of rail electrification systems. Most railway electrical systems rely on contact, either through pantographs – the connectors used by electric trains to link to overhead power cables – or ground-based connectors, both of which are costly to install and maintain. Professor Sun will develop new contact, hybrid and contactless electrification systems. These will include an integrated fibreoptic sensor system enabling continuous all-weather monitoring of these high voltage systems while they are in operation.

Professor Philip Torr, University of Oxford

Five AI/ Royal Academy of Engineering Research Chair in Computer Vision

Professor Torr’s research focuses on computer vision and its use in autonomous vehicles. The project brings together state-of-the-art object recognition, tracking and 3D reconstruction technologies to place the UK at the forefront of autonomous vehicle innovation. This includes developing novel computer vision and machine learning algorithms to improve real-time detection, and creating deep neural networks that learn from visual data and can “teach” vehicles to recognise objects and understand the intentions of other road users. As chief scientific advisor to Five AI, Professor Torr’s work will feed in to the delivery of a shared autonomous vehicle service created for European cities, with trials starting in London in 2020.

Professor Tom Vercauteren, King’s College London

Medtronic/ Royal Academy of Engineering Research Chair in Machine Learning for Computer-Assisted Neurosurgery

Professor Tom Vercauteren will design new machine learning technologies to provide neurosurgeons with accurate and timely information to help plan, deliver and monitor surgical procedures. Neurosurgeons use medical imaging techniques, such as MRI, CT, ultrasound and fluorescence imaging, to identify the best surgical approach and for real-time navigational guidance. These images provide vast amounts of data that must be minutely exploited to be effective. The artificial intelligence systems developed by Professor Vercauteren will act as smart virtual surgical assistants, providing interactive, patient-specific decision support that incorporates real-time feedback from the clinical team.