• Feature-Enhanced Representation with Transformers for Multi-View Stereo 
• High-Frequency Channel Attention and Contrastive Learning for Image Super-Resolution 
• A Divide-and-Conquer Machine Learning Approach for Modelling Turbulent Flows 
•  
• DRLFluent: A distributed co-simulation framework coupling deep reinforcement learning with Ansys-Fluent on high-performance computing systems 
• Manifold-enhanced CycleGAN for facial expression synthesis 

To discuss this research or potential partnerships, contact Professor Yin at hujun.yin@manchester.ac.uk.
 

The funding package from the University’s Strategic Investment Reserve Fund (SIRF) is being matched by £2 million from the Faculty itself. The investment will go towards appointing an interdisciplinary team of six senior lecturer or lecturer-level academics, six post-doctoral research associates and six PhD students. They will form a cross-cutting research cluster with the (CDTS) at the University.

The investment will also leverage further research and industry funding, and help develop new teaching and executive education programmes, strengthening the University’s capability in ethical and responsible AI.

Professor Fiona Devine, Vice-President and Dean of the Faculty of Humanities, said: “I am absolutely delighted that the Faculty has been successful in securing this funding to significantly expand and enhance our research and teaching capabilities in this emerging field. The investment is designed to retain our status as a UK leader in cyber security and responsible AI research and teaching.”

Richard Allmendinger, Professor of Applied Artificial Intelligence at Alliance 91ֱ�� Business School (AMBS), and Faculty Associate Dean for Business Engagement, Civic and Cultural Partnerships, said: “This investment comes at a critical juncture and gives the Faculty of Humanities a critical mass in social science-led approaches to AI which will enable us to maximise external research funding opportunities.

“The demand from industry is clear. International partners wish to collaborate on issues of AI governance and responsible AI, as do various strategic partners. As a city-region, 91ֱ�� also has the by number of jobs outside London.”

Professor Nick Lord, Director of the CDTS, and Professor of Criminology in the School of Social Sciences, added: “AI is already having a profound effect on society and will continue to do so, and that means impacting everything we do as a University, too. To mitigate risks and ensure the benefits of AI technologies we must consider the social, environmental and economic contexts they will operate in, and the consequences of their deployment.

“There is an urgent need to drive approaches to AI that are secure, safe, reliable and trustworthy. It is also vital that they operate in a way that enables us to understand and investigate when they fail.”

Enhancing Faculty of Humanities research power in AI trust and security will also catalyse new collaborations with the Faculty of Biology, Medicine and Health at the University, most notably with the for health technology research and innovation.

Added Professor Devine: “The complexity and rise of data in healthcare means that AI will increasingly be applied within the field and has the potential to speed up diagnostics and make healthcare operations more efficient.

“Humanities research has much to contribute to this truly inter-disciplinary agenda and this investment will establish the University of Manchester as a leader in ethical, assessable, inclusive and responsible AI. It aligns not only with our commitment to cutting-edge research and innovation but also with our commitment to social responsibility.”

The AI Trust and Security team will form a cross-cutting research cluster within the CDTS. The new initiative follows the recent announcement that the University of Manchester was awarded the status of by the National Cyber Security Centre and the Engineering and Physical Sciences Research Council. The Centre is distinctive as it is the only cyber and digital security and trust research centre in the UK led from social science, rather than computer science or engineering.

Meanwhile, demand for new teaching programmes in the area of AI is also soaring, as demonstrated by the recent review of the .

Data from April 2020 to March 2023 shows 7,600 students have enrolled on AI and data science postgraduate conversion courses across the UK, helping to address a critical digital skills gap in the AI and data science industries.

The University of Manchester has been awarded £30 million funding by the Engineering and Physical Sciences Research Council (EPSRC) for four Centres for Doctoral Training as part of the UK Research and Innovation’s (UKRI) £500 million investment in engineering and physical sciences doctoral skills across the UK.

Building on 91ֱ��’s long-standing record of sustained support for doctoral training, the new CDTs will boost UK expertise in critical areas such as advanced materials, AI, and nuclear energy.

The CDTs include:

• EPSRC Centre for Doctoral Training in 2D Materials of Tomorrow (2DMoT) - with cross-disciplinary research in the science and applications of two-dimensional materials, this CDT will focus on a new class of advanced materials with potential to transform modern technologies, from clean energy to quantum engineering. Led by , Professor of Physics at The University of Manchester.
   
• EPSRC Centre for Doctoral Training Developing National Capability for Materials 4.0 - this CDT will bring together students from a range of backgrounds in science and engineering to drive forward the digitalisation of materials research and innovation. Led by , Professor of Applied Mathematics at The University of Manchester and the Henry Royce Institute.
   
• EPSRC Centre for Doctoral Training in Robotics and AI for Net Zero - this CDT will train and develop the next generation of multi-disciplinary robotic systems engineers to help revolutionise lifecycle asset management, in support of the UK’s Net Zero Strategy. Led by , Reader in the Department of Electrical and Electronic Engineering at The University of Manchester.
   
• EPSRC Centre for Doctoral Training in SATURN (Skills And Training Underpinning a Renaissance in Nuclear) - the primary aim of SATURN is to provide high quality research training in science and engineering, underpinning nuclear fission technology. Led by , Professor of Nuclear Chemistry at The University of Manchester.

91ֱ�� received joint-third most awards across UK academia, and will partner with University of Cambridge, University of Glasgow, Imperial College London, Lancaster University, University of Leeds, University of Liverpool, University of Oxford, University of Sheffield, University of Strathclyde and the National Physical Laboratory to prepare the next generation of researchers, specialists and industry experts across a wide range of sectors and industries.

In addition to leading these four CDTs, The University of Manchester is also collaborating as a partner institution on the following CDTs:

• EPSRC Centre for Doctoral Training in Fusion Power, based at University of York.
• EPSRC Centre for Doctoral Training in Aerosol Science: Harnessing Aerosol Science for Improved Security, Resilience and Global Health, based at University of Bristol.
• EPSRC Centre for Doctoral Training in Compound Semiconductor Manufacturing, based at Cardiff University.

Along with institutional partnerships, all CDTs work with industrial partners, offering opportunities for students to develop their skills and knowledge in real-world environments which will produce a pipeline of highly skilled researchers ready to enter industry and take on sector challenges.

Professor Scott Heath, Associate Dean for Postgraduate and Early Career Researchers at The University of Manchester said of the awards: “We are delighted that the EPSRC have awarded this funding to establish these CDTs and expose new cohorts to the interdisciplinary experience that researching through a CDT encourages. By equipping the next generation of researchers with the expertise and skills necessary to tackle complex issues, we are laying the groundwork for transformative solutions that will shape industries and societies for generations to come.”

Announced by Science, Innovation and Technology Secretary Michelle Donelan, this round of funding is the largest investment in engineering and physical sciences doctoral skills to-date, totalling more than £1 billion. Science and Technology Secretary, Michelle Donelan, said: “As innovators across the world break new ground faster than ever, it is vital that government, business and academia invests in ambitious UK talent, giving them the tools to pioneer new discoveries that benefit all our lives while creating new jobs and growing the economy.

“By targeting critical technologies including artificial intelligence and future telecoms, we are supporting world class universities across the UK to build the skills base we need to unleash the potential of future tech and maintain our country’s reputation as a hub of cutting-edge research and development.”

These CDTs join the already announced . This CDT led by , Senior Lecturer in Machine Learning at The University of Manchester, will train the next generation of AI researchers to develop AI methods designed to accelerate new scientific discoveries – specifically in the fields of astronomy, engineering biology and material science.

The first cohort of AI CDT, SATURN CDT and Developing National Capability for Materials 4.0 CDT students will start in the 2024/2025 academic year, recruitment for which will begin shortly. 2DMoT CDT and RAINZ CDT will have their first cohort in 2025/26.

For more information about the University of Manchester's Centres for Doctoral Training, please visit:

The framework combines dimension reduction techniques and a new explainable clustering algorithm called CLASSIX, developed by mathematicians at The University of Manchester. This enables the quick identification of groups of viral genomes that might present a risk in the future from huge volumes of data.

, presented this week in the journal PNAS, could support traditional methods of tracking viral evolution, such as phylogenetic analysis, which currently require extensive manual curation.

Like many other RNA viruses, COVID-19 has a high mutation rate and short time between generations meaning it evolves extremely rapidly. This means identifying new strains that are likely to be problematic in the future requires considerable effort.

Currently, there are almost 16 million sequences available on the GISAID database (the Global Initiative on Sharing All Influenza Data), which provides access to genomic data of influenza viruses.

Mapping the evolution and history of all COVID-19 genomes from this data is currently done using extremely large amounts of computer and human time.

The described method allows automation of such tasks. The researchers processed 5.7 million high-coverage sequences in only one to two days on a standard modern laptop; this would not be possible for existing methods, putting identification of concerning pathogen strains in the hands of more researchers due to reduced resource needs.

, Professor of Mathematical Sciences at The University of Manchester, said: “The unprecedented amount of genetic data generated during the pandemic demands improvements to our methods to analyse it thoroughly. The data is continuing to grow rapidly but without showing a benefit to curating this data, there is a risk that it will be removed or deleted.

“We know that human expert time is limited, so our approach should not replace the work of humans all together but work alongside them to enable the job to be done much quicker and free our experts for other vital developments.”

The proposed method works by breaking down genetic sequences of the COVID-19 virus into smaller “words” (called 3-mers) represented as numbers by counting them. Then, it groups similar sequences together based on their word patterns using machine learning techniques.

, Professor of Applied Mathematics at The University of Manchester, said: “The clustering algorithm CLASSIX we developed is much less computationally demanding than traditional methods and is fully explainable, meaning that it provides textual and visual explanations of the computed clusters.”

Roberto Cahuantzi added: “Our analysis serves as a proof of concept, demonstrating the potential use of machine learning methods as an alert tool for the early discovery of emerging major variants without relying on the need to generate phylogenies.

“Whilst phylogenetics remains the ‘gold standard’ for understanding the viral ancestry, these machine learning methods can accommodate several orders of magnitude more sequences than the current phylogenetic methods and at a low computational cost.”

The highly interdisciplinary team used cutting-edge unsupervised deep learning to analyse over 50,000 three-dimensional magnetic resonance images of the heart from UK Biobank, a world-leading biomedical database and research resource.

The study, published in the leading journal , focused on uncovering the intricate genetic underpinnings of cardiovascular traits. The research team conducted comprehensive genome-wide association studies (GWAS) and transcriptome-wide association studies (TWAS), resulting in the discovery of 49 novel genetic locations showing an association with morphological cardiac traits with high statistical significance, as well as 25 additional loci with suggestive evidence.  

The study's findings have significant implications for cardiology and precision medicine. By elucidating the genetic basis of cardiovascular traits, the research paves the way for the development of targeted therapies and interventions for individuals at risk of heart disease.

The research was funded by the Royal Academy of Engineering (RAEng), The Royal Society, the British Heart Foundation (BHF), and the Argentinean National Scientific and Technical Research Council (CONICET) in an interdisciplinary collaboration involving a RAEng Chair, two BHF Professors, and an IBM Fellow.

The research was directed by , Director of the , the Bicentennial Turing Chair for Computational Medicine, and a Royal Academy of Engineering Chair in Emerging Technologies. The first author was Rodrigo Bonazzola, a PhD candidate, jointly co-supervised by Prof Frangi, (CONICET, Argentina) (IBM Fellow and Chief Scientist at IBM Research).

Prof Frangi said: “This is an achievement which once would have seemed like science fiction, but we show that it is completely possible to use AI to understand the genetic underpinning of the left ventricle, just by looking at three-dimensional images of the heart.

“Previous studies have only investigated association of traditional clinical phenotypes, such as left ventricular mass or stroke volume, limiting the number of gene associations detected for a given study size. However, this study used AI not only to delineate the cardiac chambers from three-dimensional medical images at pace but also to unveil novel genetic loci associated with various cardiovascular deep phenotypes.”

He added: “This research exemplifies the power of multidisciplinary teams and international collaborations, bolstered by UK Biobank's valuable data. By marrying genetic data with cardiac imaging through advanced machine learning, we've gained novel insights into the factors shaping cardiovascular health.”

Early career scientist and rising star, Bonazzola, the study's lead author said: “Our research reveals genes that harbour mutations known to be detrimental to other organisms, yet the impact of common variations within these genes on cardiac structure across the human population had not been previously documented. For instance, the STRN gene, recognised for its harmful variants leading to dilated cardiomyopathy in dogs, exhibits a common variant in humans that seems to induce a subtle but detectable change in mitral orientation.”

Dr Ferrante said: “The study's core achievement is a robust method based on geometric deep learning for large-scale genetic and cardiac imaging data analysis, leading to ground-breaking genetic insights related to heart structure. These discoveries could revolutionize our approach to disease understanding, drug development, and precision medicine in cardiology. The study's thorough analysis and ensemble-based methods also enhance the discovery rates and the reliability of our findings.”

Prof Keavney, BHF Professor of Cardiovascular Medicine at The University of Manchester, emphasised the transformative methodology. He said: “Employing cutting-edge deep learning to integrate genetic and imaging data has shed light on the genetic underpinnings of heart structure. This approach is a beacon for future organ studies and understanding genetic influences on organ anatomy.”

Prof Plein, BHF Professor of Cardiovascular Imaging in Leeds, said: "Cardiovascular MRI plays a crucial role in understanding disease phenotypes, allowing us to uncover genetic associations that help stratify cardiovascular diseases, ultimately leading to better treatments and precision medicine."

Professor Frangi added: “Our publication marks a significant stride in correlating deep cardiovascular imaging traits with genetic data. It paves the way for revolutionary progress in cardiovascular research, clinical practices, and tailored patient care.”

Professor Bryan Williams, Chief Scientific and Medical Officer at the British Heart Foundation, said: “This new research shows the huge power of big data linking genes to heart structure. Machine learning has made this possible by transforming how we process, analyse and gain insights from big data to tackle the biggest questions in cardiovascular research. This pioneering new method has uncovered many more genes that influence the structure and function of the heart, which will lead to new insights into why abnormal structure and function can lead to heart disease.

“Heart and circulatory diseases are still devastating millions of lives each year in the UK. AI could unlock more information about the genes that contribute to the structure of the heart. In future this could lead to real improvements for patients, including the development of tailored, precision treatments for people with heart problems.”

The paper Unsupervised ensemble-based phenotyping enhances discoverability of genes related to left-ventricular morphology is published in

The evolution of solutions is creating new opportunities to transform patient care and personal health outcomes. From remote monitoring and wearables, to artificial intelligence and machine learning, digital technologies are enabling health data collection and analysis and offering new insights, diagnosis and therapies.

Here is an overview of the Citation Impact on Digital Health Top 25 Rankings. The complete list can be accessed in ’s article.

Research into digital health has grown massively nowadays, whereas the scale of growth in digital health research is remarkable. Based on Clarivate data, publications on digital health topics – which include everything from wearable devices and mobile apps to AI analytics, telemedicine and 3D printing of drugs – have risen nearly 70-fold between 2013 and 2022, from a mere 39 Web of Science-indexed papers to 2,641 – while UK researchers were involved in 20 per cent of all papers.

The statistics demonstrate that the University currently has 75 digital health papers in the Web of Science, 1582 citations, 32 per cent of papers in the top 10 per cent by citation, scoring 2.50 category normalised citation impact (CNCI). It showcases 91ֱ��’s consistent efforts to advance digital health research that benefits the public.

Previously, the immense volumes of medical data from numerous wearable devices or mobile phones might have overwhelmed even the most data-savvy researcher. However, artificial intelligence now enables researchers to effectively navigate such vast amounts of information without requiring advanced coding skills. Likewise, hospitals and health centres worldwide are sharing patient records in a manner that allows algorithms to detect trends, including identifying emerging pandemics at their onset.

Recent University of Manchester research, alongside Oxford University and Cancer Research UK used Artificial Intelligence to reveal a new form of aggressive prostate cancer which could revolutionise how the disease is diagnosed and treated in the future.

For more information:

A new Cancer Research UK-funded study has revealed that prostate cancer, which affects one in eight men in their lifetime, includes two different subtypes termed evotypes.

The discovery was made by an international team led by the , and The University of Manchester, who applied AI (artificial intelligence) on data from DNA to identify two different subtypes affecting the prostate.

The team hope their findings could save thousands of lives in future and revolutionise how prostate cancer is diagnosed and treated. Ultimately, it could provide tailored treatments to each individual patient according to a genetic test which will also be delivered using AI.

According to , prostate cancer is the most common cancer affecting men in the UK with around 52,000 cases a year. Dr Rupal Mistry, the charity’s senior Science Engagement Manager, said:

“The work published today by this global consortium of researchers has the potential to make a real difference to people affected by prostate cancer. The more we understand about cancer the better chance we have of developing treatments to beat it. We are proud to have helped fund this cutting-edge work, which has laid the foundations for personalised treatments for people with prostate cancer, allowing more people to beat their disease.”

The ground-breaking research, which involved additional funding from Prostate Cancer Research and involved scientists from the University of Oxford the University of Manchester, the University of East Anglia and the Institute of Cancer Research, London, highlights how a prostate cancer diagnosis can affect physical, emotional and mental wellbeing.

Lead researcher Dr Dan Woodcock, of the Nuffield Department of Surgical Sciences at the University of Oxford, said: “Our research demonstrates that prostate tumours evolve along multiple pathways, leading to two distinct disease types. 

“This understanding is pivotal as it allows us to classify tumours based on how the cancer evolves rather than solely on individual gene mutations or expression patterns.” 

The researchers worked together as part of international consortium, called The Pan Prostate Cancer Group, set up by scientists at The Institute of Cancer Research (ICR) and The University of East Anglia to analyse genetic data from thousands of prostate cancer samples across nine countries. 

Crucially, the team's collaboration with Cancer Research UK (CRUK) aims to develop a genetic test that, when combined with conventional staging and grading, can provide a more precise prognosis for each patient, allowing tailored treatment decisions. 

The researchers used AI to study changes in the DNA of prostate cancer samples (using whole genome sequencing) from 159 patients. 

They identified two distinct cancer groups among these patients using an AI technique called neural networks. These two groups were confirmed by using two other mathematical approaches applied to different aspects of the data. This finding was validated in other independent datasets from Canada and Australia. 

They went on to integrate all the information to generate an evolutionary tree showing how the two subtypes of prostate cancer develop, ultimately converging into two distinct disease types termed ‘evotypes’. 

of Manchester Cancer Research Centre, who led the study, explained: “This realisation is what enables us to distinguish the disease types. This hasn’t been done before because it’s more complicated than HER2+ in breast cancer, for instance. 

"This understanding is pivotal as it allows us to classify tumours based on their evolutionary trajectory rather than solely on individual gene mutations or expression patterns." 

Researcher Prof Colin Cooper, from UEA’s Norwich Medical School, highlighted that while prostate cancer is responsible for a large proportion of all male cancer deaths, it is more commonly a disease men die with rather than from. This means that unnecessary treatment can often be avoided, sparing men from side-effects such as incontinence and impotence. 

He added: “This study is really important because until now, we thought that prostate cancer was just one type of disease. But it is only now, with advancements in artificial intelligence, that we have been able to show that there are actually two different subtypes at play. 

“We hope that the findings will not only save lives through better diagnosis and tailored treatments in the future, but they may help researchers working in other cancer fields better understand other types of cancer too.” 

Dr Naomi Elster, Director of Research at Prostate Cancer Research, said: “We simply don’t know enough about what a prostate cancer diagnosis means at present – there are many men who have disease which is or may become aggressive and being able to treat aggressive disease more effectively is critical. But on the other side of the coin are the too many men who live with side effects of cancer treatment they may never have needed. 

“These results could be the beginning of us being able to take the same ‘divide and conquer’ approach to prostate cancer that has worked in other diseases, such as breast cancer.” 

Professor Ros Eeles, Professor of Oncogenetics at The Institute of Cancer Research, London, and Honorary Consultant in Clinical Oncology and Cancer Genetics at The Royal Marsden NHS Foundation Trust, said: “This study has utilised the enormous genomic dataset from The Pan Prostate Cancer Group – a powerhouse of information about prostate cancer from around the world. These results will hopefully lead to better treatments for patients, demonstrating the importance of data sharing and team science.”

The study - ‘’ is published online in the journal Cell Genomics.

The EPSRC AI Hub for Causality in Healthcare AI with Real Data (CHAI) will develop new ways of unearthing important links in complex health data.

The hub will develop ways to use AI to enable the early prediction of debilitating diseases thanks to the £12m funding from the Engineering and Physical Sciences Research Council (EPSRC).

It is part of the nine centres announced as part of EPSRC’s £80m UK-wide investment in applying AI to real world data and research.

CHAI aims to develop AI that can empower decision making tools to improve challenging tasks such as the early prediction, diagnosis and prevention of disease, and – crucially – to improve the safety of such technology in healthcare.

Researchers hope to apply this new technology to tackle key societal health challenges such as understanding infection, Alzheimer’s, improving cancer treatments, social care, diabetes, and rehabilitation.

CHAI will be led by The University of Edinburgh’s Professor Sotirios Tsaftaris, Canon Medical/RAEng Chair in Healthcare AI.

Professor Tsaftaris said: “I'm delighted that the University of Edinburgh will be leading this world-leading consortium to develop next generation Causal AI. Causal AI holds tremendous promise for creating a new generation of AI solutions that are more robust, fair, safe, and transparent. Causal AI offers a step change in what AI can do for health with the proper safeguards. To fulfil this vision CHAI brings together an incredible team from across the UK (Imperial, 91ֱ��, UCL, Exeter, KCL), several affiliated researchers and domain experts, as well as more than 50 world-leading partner organisations to work together to co-create solutions thoroughly integrating ethical and societal aspects.

“I am extremely excited to lead this hub, particularly because of the strong people focus ensuring that we prepare the next generation of researchers in such cutting-edge AI methods.”

, who directs the University of Manchester part of the hub said: “I am so excited to be a part of the new CHAI hub. The focus on causality aligns with key strengths at the University, and ensures that we can build AI for healthcare that is robust, fair, and directly applicable to decision support. This is a genuine opportunity for us to transform the role of AI in health.”

Though methotrexate is the first-line drug to be given for Juvenile idiopathic arthritis (JIA), it is only effective or tolerated in half of the children and young people who receive it.

Those patients not helped by the drug have to wait longer to receive second line therapies, potentially prolonging the severe joint pain and other symptoms which often have a devastating impact on children and their families.

The study, published in the journal eBioMedicine, could facilitate more precise research into the identification of response predictors to methotrexate, such as biomarkers, and lead to better forecasting of likely outcomes following drug initiation.

It confirms that one in eight children and young people starting methotrexate will demonstrate improvements in inflammatory features of disease yet have some symptoms.

They also showed that in 16 per cent of children taking methotrexate, improvements in disease activity could be slower than in others over time.

Lead author said: “Giving methotrexate to children who it will not help wastes time, money and effort for healthcare services-  as well as unnecessarily exposing them to potential side effects.

“But now machine learning has opened the door made it  possible to predicting which aspects of a child’s disease would be helped by the drug and so which children should start other therapies either alongside or instead of methotrexate straight away.

“In addition, this work shows how clinical trials are missing the mark in only looking at drug ‘response’ or ‘non-response’ for childhood-onset arthritis.

“This oversimplification could lead to a drug being labelled as ‘effective’ when key symptoms such as pain remain, or ‘ineffective’ where a significant improvement is seen in one aspect of this complex disease.”

The research is funded by the Medical Research Council, Versus Arthritis, Great Ormond Street Hospital Children’s Charity, Olivia’s Vision, and the National Institute for Health Research as part of the CLUSTER consortium.

The research team accessed data from four nationwide cohorts of children and young people who began their treatment before January 2018.

Juvenile arthritis disease activity score components (including how many swollen joints, a doctor’s perception of disease, a patient/parent report of wellbeing, results of a blood test for inflammation) were recorded at the start of treatment  and over the following year.

They used machine learning identify clusters of patients with distinct disease patterns following methotrexate treatment, predict clusters; and compare clusters to existing treatment response measures.

From 657 children and young people verified in 1241 patients they identified Fast improvers (11%), Slow Improvers (16%), Improve-Relapse (7%), Persistent Disease (44%).

Two other clusters they called Persistent physician global assessment (8%) and Persistent parental global assessment  (13%), were characterised by improvement in all activity score features except one.

Dr Shoop-Worrall added: “The longer-term impact of this slower disease control needs further investigation. Our study also demonstrates the utility of machine learning methods to uncover clusters of children as a basis for stratified treatment decisions.

“This work builds on existing studies of methotrexate treatment response, confirming that response is not bivariate but can be highly variable across different features of disease within individuals.

“At the moment trials of methotrexate in JIA categorise patients into responders and non-responders.

“That misclassification can compromise studies looking to identify predictors of response, such as biomarkers.”

• The paper Towards Stratified Treatment of JIA: Machine Learning Identifies Subtypes in Response to Methotrexate from Four UK Cohorts is available
• Image shows open bottle of methotrexate drug—one of the first chemotherapeutic drugs used in the early 1950s (released by the National Cancer Institute in the US,  part of the National Institutes of Health)

The purpose of the consortium will be to provide Sellafield Ltd with technical support as it delivers its long-term objectives of safely inspecting and decommissioning their facilities using remote technologies.

Sellafield Ltd have made considerable progress with the deployment of robots to address challenges on its site. However, there are many challenges that remain, many of which cannot be solved using currently available commercial technologies.

The academic consortium will be led by Professor Barry Lennox and Dr Simon Watson at The University of Manchester and supported by groups at The University of Bristol, led by Professor Tom Scott, and The University of Oxford, led by Professor Nick Hawes. Sellafield Ltd’s engagement with the academic consortium will be led by its Robotics and Manufacturing Lead, Dr Melissa Willis.

Melissa Willis, Robotics and Manufacturing Research Lead at Sellafield Ltd, added: “We are excited by the opportunities that this consortium provides us with and are confident that their technical expertise will help us to deliver the benefits that robotics technology offers us on the Sellafield site.”

The consortium has considerable experience of working with Sellafield Ltd, having all been involved in the RAIN (Robotics and Artificial Intelligence for Nuclear) hub, and more recently The University of Manchester has provided the academic leadership for the Robotics and AI Collaboration (RAICo) in Cumbria.

Experience of the consortium includes the design, development and deployment of mobile robots in a range of air, land and aquatic environments in the UK and overseas.

Working collaboratively with Sellafield Ltd, researchers at The University of Manchester developed AVEXIS, which can be deployed into aquatic facilities with access ports as small as 150 mm and collect visual and radiometric data. The commercial version of AVEXIS was the first robot to be deployed into Sellafield’s Magnox Swarf Storage Silos and its use at Fukushima Daiichi has been explored.

The University of Oxford’s Robotics Institute (ORI) have developed a range of mapping and mission planning technologies that can be used by robots, such as Boston Dynamics’ Spot to autonomously monitor facilities and identify unexpected changes.

Using quadrotor and fixed wing vehicles, the University of Bristol have developed technology able to map radioactivity levels over large areas of land. The technology has been deployed successfully in the UK and overseas, with the image showing a radiation dose map generated over the Red Forest area of the Chornobyl Exclusion Zone, Ukraine, with the orange/red areas showing regions of elevated gamma dose rates.

Viscount Camrose started his tour at Engineering Building A, home to the new international research centre CRADLE (Centre for Robotic Autonomy in Demanding and Long-lasting Environments), where he announced the countdown to the centre’s official opening in November.

The Minister was guided by Professor Barry Lennox, The University of Manchester’s Centre for Robotics and AI Co-Director, where he learnt all about the interdisciplinary research going on in the centre, including a demonstration of a robot named Lyra, built to help transform nuclear infrastructure inspection.

Lyra was used to survey one of the radiologically contaminated ducts in Dounreay. It performed the equivalent of more than 400 air-fed suited entries into the site, equal to 2,250 man-hours. This capability reduced costs by an estimated £5m and it is predicted that similar surveys could save decommissioning costs by a further £500m in the future.

The Minister then took a tour of the Graphene Engineering Innovation Centre (GEIC), taking in its energy storage labs, printing lab facilities and construction materials testing facility, before making his way to ID 91ֱ�� and the location for the (TIC); a project which aims to link businesses to cutting-edge AI research and technologies to help enhance productivity.

John Holden, Associate Vice-President for Major Special Projects at The University of Manchester, said: “I was delighted to welcome the minister to The University of Manchester and to show him the leading-edge research and development activity we are undertaking in areas critical to the UK’s future economic growth and prosperity, including our pioneering work in AI and robotics.

“Funding research and development in universities is critical to regional and national efforts to improve productivity across all industries, and the visit was an opportunity to highlight to the minister how we are accelerating the translation of our research base into industrial application through initiatives such as GEIC and the Turing Innovation Catalyst.

“The visit was also an opportunity to highlight the major opportunity that ID 91ֱ�� represents for the region and UK – our plan to transform eight hectares of the North Campus into a commercially-led innovation district will create a world-leading innovation ecosystem around the University and has the potential to create 10,000 high quality jobs in research and development intensive sectors linked to the University’s capabilities over the next 10-15 years.”

The Minister for AI and Intellectual Property, Viscount Camrose, added: “Greater 91ֱ�� has long been at the forefront of science and innovation in this country, from the first splitting of the atom to the invention of the first computer.

“By engaging closely with partners including The University of Manchester, businesses and local government, we can continue to grow our innovation economy across the country and level-up the UK.

“It was great to see first-hand some of the fantastic Government-backed research in 91ֱ��, such as the development of graphene applications at the GEIC, CRADLE’s cutting-edge innovations in robotics, as well as some of the projects underway through our £100m Innovation Accelerators programme such as the Turing Innovation Catalyst, the Centre for Digital Innovation and the Immersive Technologies Innovation Hub.”

The visit ended with a round-table discussion about the . Led by Innovate UK on behalf of the Department for Science, Innovation Technology (DSIT), the pilot programme is investing £100m in 26 transformative R&D projects to accelerate the growth of three high-potential innovation clusters – Greater 91ֱ��, Glasgow City Region and the West Midlands.

Leaders from three AI-related projects backed by the Innovation Accelerator – the Turing Innovation Catalyst, led by The University of Manchester, the Centre for Digital Innovation, led by 91ֱ�� Metropolitan University, and the MediaCity Immersive Technologies Innovation Hub, led by The Landing at MediaCityUK – attended the round-table. They were joined by Cllr Bev Craig, Leader of Manchester City Council and Greater 91ֱ�� lead for Economy, Business and International, and representatives from Greater 91ֱ�� Combined Authority (GMCA).

Participants discussed how to strengthen connections between these projects and maximise their value, and other national initiatives to support AI and related technologies.

Cllr Bev Craig, Leader of Manchester City Council and GMCA Lead for Economy and Business, said: “Today’s visit provided a fantastic opportunity for the minister to learn more about the groundbreaking research and innovation happening right here in Greater 91ֱ��, and particularly at The University of Manchester.

“In recent years we have grown a reputation as a leading digital city-region, with AI as an important emerging sub-sector. As the impact of AI on our economy and society continues to grow, Greater 91ֱ�� is well-placed, with the potential to go even further.

“We also held a productive discussion about Greater 91ֱ��’s Innovation Accelerator programme and its AI-related projects. Through the Innovation Accelerator we are piloting a new model of R&D decision making that empowers local leaders to harness innovation in support of regional economic growth.”