A new era of high-intensity laser experiments has begun. Recent UK experiments, in which beams of ultra-relativistic electrons were collided with intense laser pulses, have shown that it is possible not only to use intense lasers to probe fundamental physics, but also to generate radiation sources with unique properties, which find applications across the sciences. Such experiments are extremely challenging, and despite recent successes there is disagreement over to what extent quantum effects have been observed. Discrepancies between experimental results and theoretical predictions have been attributed to the numerical models of quantum effects employed in Particle-In-Cell (PIC) codes used to simulate and analyse experiments. A host of new experiments will begin this year, and will be able to probe the transition from classical to quantum physics in intense electromagnetic fields. It is therefore critical that we improve our understanding of theoretical models, and their implementations, in order to ensure that theoretical predictions and analyses keep up with experimental progress. To meet this urgent experimental demand we propose developing existing theory on two fronts. On one front, we will extend existing models to include currently neglected processes (such as absorption and trident pair production) in a systematic way that can be immediately employed by simulators. On the second front, we will analyse a number of quantum effects which cannot be captured by existing numerical models (but which become relevant in e.g. the overlapping field geometries of future facilities, or in dense electron bunches), assess their importance to experimental campaigns, and develop a methodology to implement them numerically, going beyond current models. Doing so requires a team of researchers who are not only experts in the theory of quantum effects in intense laser physics, but who also have the experience required to understand numerical implementation and experimental analyses. This is not a case of benchmarking existing codes, already well-covered in the literature. What is needed, rather, is a "top down", approach which can verify, and improve upon, the models of quantum effects which are used in the codes. Plymouth hosts an established, world-leading research group in the area of intense laser-matter interactions. Staff members are research-active and well-known in the community as experts in the theory of quantum effects in intense laser physics. Furthermore, the Investigators attached to this project are actively involved in experimental efforts, being for example part of the team which recently demonstrated radiation reaction in laser-matter collisions in an experiment at the UK's Central Laser Facility. As such the Investigators have precisely the right skillset to undertake this timely project and deliver new results of import to a wide community of physicists. This will help maintain the UK's world-leading capabilities in the active research area of intense laser-matter interactions.

Programming with confidential data is ubiquitous in the modern world. For instance, imagine using credit card information for e-commerce, and passwords or private keys for authentication. Bugs in secret-handling code can cause information leaks, revealing sensitive, private information to untrusted parties. Corporations often spend huge amount of resources. to ensure the secrecy of the data being handled by the program. Despite all this, time-and-again attackers manage to get hold of sensitive information, for instance, by exploiting untested paths or bypassing the deployed security enforcement. The average data breach was estimated to cost USD 4.35 million in 2022. This raises several fundamental questions, like 1) What does it mean for a program to be secure in a formal sense?; 2) Can we express confidentiality policies on data being programmed?; 3) Can we build methods to provide formal security guarantees wrt such confidentiality policies? and 4) Finally, can we ensure that such security guarantees are preserved even after compilation? (i.e. argue that the compiler itself has not introduced a covert channel thwarting the guarantees offered before compilation)? The proposal aims to answer the questions like above by building methods and tools for providing provable guarantees about program confidentiality (even in the presence of deliberate information disclosure, as is often required in practice). The proposed work aims for two key deliverables 1. A programming language in which every program is provably secure by construction. 2. A compiler that ensures that security guarantees are preserved even after compilation.

Patients with blood disorders, can be treated by stem cell transplants from a third party. Finding an adult match for such patients is not always possible, but alternatives such as umbilical cord blood (UCB) can be used for transplantation. UCB are readily available and stored in frozen cell banks around the world. However, UCB transplants show delayed and, sometimes, insufficient engraftment of the patient's haematopoietic system. In this study, we will investigate characteristics of UCB from different donors to find biomarkers, which are associated with better engraftment in a pre-clinical animal model and in patients who received UCB transplants. We will employ cutting-edge cellular and molecular biology analyses and implement in-depth artificial intelligence (machine learning) methodology in order to find the biomarkers and develop robust test for selection of UCB units, which would work best in patients and reduce the number of failed transplants.

Artificial Intelligence (AI) is transforming the world. AI is the core technology of many of the biggest companies in the world, Amazon, Google, Facebook, etc. that effect all our lives. AI is now starting to transform science and technology. Most people in the EU now live better than Kings did in the past: they have better food, medical care, transport, etc. This miracle has been made possible through better technology based on science. To meet the great challenges the 21st century world faces: climate change, food insecurity, disease, etc., we need to make science and technology even more efficient. We propose the AMBITION project to harness the power of AI and laboratory robotics to provide researchers in the UK, and beyond, with continuous, uninterrupted, remote access to AI/robotic augmented biomedical research capabilities. This will enable more robust, efficient and reproducible biomedical research. The UK's life sciences, biotechnological and pharmaceutical industry are world-leading. However, the Covid-19 pandemic has clearly demonstrated the vital importance of biomedical research and the critical need to maintain research continuity at all times. Yet, lockdowns and social distancing pose a severe threat to research continuity, forcing laboratories to shut down, risking loss of years of research. Integrating AI with laboratory automation will also enable the automation of routine parts of scientific theory formation and experimentation. This will enable results to be obtained both more efficiently and faster compared to the state-of-the-art where human scientists must make all the decisions. AMBITION does not aim to replace humans, but empower them by reasoning and data processing capabilities to better support their decision making. Biomedical science is facing a 'reproducibility crisis'. Despite reproducibility being fundamental to science, the reproducibility of few biomedical results is currently tested, and when reproducibility is tested, the results are dismal, with only 10 to 20% of published biomedical research found to be reproducible. Finally, automated laboratories will make scientific results more reproducible, as AI systems describe experiments in more clearly than human scientists, and robots execute experimental protocols more accurately than human scientists. The project will focus on the development of the AI part of the system and iterative testing in real-world laboratory settings employing state-of-the-art robotics equipment. We will initially focus on cancer drug discovery as a first demonstration case, bringing together the power of AI and laboratory robotics. In the medium-term (3-5 years horizon). We plan to extend the approach to clinical patient care, and to provide real-time cancer treatment decision support system for patients in the UK and beyond based on automated testing of hundreds of treatment options on patient-derived tumour material, thereby leading to a reduction in animal experimentation, and giving clinicians an evidence-based, real-time input for their expert treatment decision. In the long-term (5-15 years horizon) we will rollout automated research capabilities and real-time treatment guidance across all of biomedicine, especially fields such as antibiotic treatment/ antimicrobial resistance, inflammatory diseases, etc. In 30 years, autonomous laboratories will transform the health sector. They will lower the costs of laboratory experiments, augment researchers' technical capabilities (making more elaborate and complex tests possible), reduce the risks associated with the presence of humans in the labs (working with hazardous substances, risk of infections), ensure reproducibility, increase accuracy of results, and ensure overall accountability and trust in the process. Autonomous laboratories will speed up and scale up the development of new drugs, remote testing of patients, and will be an enabler for personalised medicine.

Smart contracts are digital contracts stored on a blockchain that are automatically executed when predetermined terms and conditions are met. They are often used to automate financial transactions and thus they hold great potential to transform and disrupt the financial industry. In particular it is estimated that blockchain and smart contracts can add up to $72.2B to the UK's GDP by 2030 and create up to 700,000 new jobs by 2030. Technically, smart contracts are programs and as such, they may contain bugs. However, since smart contracts are often used to automate financial transactions, exploiting these bugs may result in huge financial damages. In 2016, for example, a vulnerability in a smart contract was exploited, resulting in a loss of approximately $60M. More recently, hackers had exploited a vulnerability in a smart contract to steal $600M. In general, it is estimated that since 2019, more than $5B were lost due to vulnerabilities in smart contracts. This threatens society's trust in smart contracts and thus limits their potential for the development of FinTech, one of the most promising sectors for the UK economy. Unfortunately, however, even rigorous testing and auditing cannot guarantee that a smart contract does what it is supposed to do. In the best case scenario, vulnerabilities can be found with these techniques but they can never guarantee their absence. Thus, with this project, we will develop tools and techniques to support the verification of smart contracts to achieve the highest degree of reliability. Finally, we will engage in training next generation experts in the verification of smart contracts by hosting a summer school on formal methods for blockchains in Exeter.