The aim of this Ph.D. project is to develop Cu-catalyzed methods for the amination of alkylboronic esters and to investigate their potential applications in biological systems. Alkylborons are valuable reagents in organic synthesis, owing to their versatile properties as building blocks for the preparation of organic molecules. These compounds are stable in air and moisture and exhibit broad compatibility and reactivity with various functional groups. The Partridge group has directed its efforts toward developing novel C-N bond-forming reactions through the interaction of alkylboronic esters and amines, thereby generating amine-containing compounds. This chemistry is particularly useful for synthesizing biologically active compounds, including pharmaceuticals, agrochemicals, and natural products. The outcomes of this research are expected to have significant implications for the pharmaceutical and rapidly expanding bioscience sectors. By enabling the discovery and preparation of probes for exploring biological systems and developing new treatments, the project is crucial for the UK's economy and the health and wellbeing of its population. Given the strength of the UK's organoboron chemistry expertise, this project is anticipated to further enhance the country's reputation in this area internationally. Through this Ph.D. project, students will acquire extensive experience in synthetic organic chemistry and catalysis, encompassing multi-step synthesis, methodology, and ATAS CERTIFICATE analytical techniques for compound characterization. The developed chemistry is expected to find applications primarily in the agrochemical and pharmaceutical industries.

Definitively characterise DNA damage in complex DNA structures, from supercoiled DNA to replication intermediates. 2. Determine how binding of key DNA repair factors (e.g. PARP) alter the structure of damaged DNA molecules.

Biomolecular simulation aims to provide quantitative, predictive models for molecular structural biology from the level of enzyme catalysis up to cells. Modelling helps us to better understand the information we obtain from our experiments, and can provide insight in situations where experimental information is unavailable. Molecular simulations are based based on well-defined physics, and complement experiments: the unique insight they provide gives molecular level understanding of how biological macromolecules function. Simulations have proved crucial in analysing protein folding and self-assembly, mechanisms of biological catalysis, and the importance of dynamics in biomolecular function. Biomolecular simulations contribute to drug development (e.g. in structure-based drug design and predictions of metabolism) and design of biomimetic catalysts, and in understanding the molecular bases of disease and drug resistance. However, the complexity of biological molecules means that simulating them is extremely challenging, and remains an active areas of research internationally. CCPBioSim is the UK network of researchers that brings together expertise to support biomolecular simulators to perform the best calculations feasible within current scientific understanding and compute resources. We actively engage with the international community of biomolecular simulators, to ensure that the UK has access to the most up to date ideas and insights. We drive methodological developments to improve the state of the art and to integrate biomolecular simulation with emerging experimental tools. We embrace technological improvements that make our simulations faster, more accurate and accessible to a broader community of researchers. We engage with our community by organising conferences and training workshops, and we provide software that makes their simulations less challenging and more effective.

The prevalence of stroke will double in 20 years. Almost a quarter of people who have stroke will develop signs of dementia after three to six months. The need for improving assessment, monitoring, and treating post-stroke cognitive impairment and dementia is listed 2nd in the top 10 priorities for stroke rehabilitation by The UK Stroke Association. This timely project aims to address this growing global challenge by improving the early detection and treatment of post-stroke dementia. This project has two main objectives: - Improve early diagnosis of dementia in stroke survivors by developing a multimodal, wearable, and intelligent assessment tool, consisting of computerised cognitive tests, brain recordings from a low-density EEG system, and CognoSpeaK [1] (our newly developed intelligent tool that automatically measures cognitive function by analysing speech) - Explore the feasibility, acceptability and usability of our newly developed P300-based brain-computer interface game [2] for improving cognitive performance in stroke survivors with and without mild cognitive impairment. To achieve the above-mentioned objectives, you will require to design and conduct experimental research, collect longitudinal data from stroke patients, process the collected data, and develop machine learning algorithms to identify robust biomarkers that could accurately monitor cognitive function and predict its failure in advance. You will work in Brain-computer Interface group at University of Sheffield. Our group is uniquely multidisciplinary and diverse, integrating exceptional research programs that span bioengineering, data science and clinical, experimental and computational neuroscience. The research vision of our group is to develop brain-directed therapies and tools for improving human's cognitive and physical performance. You will participate in a highly interdisciplinary project and interact and collaborate with system engineers, computer scientists, neuroscientist and medical doctors. You will receive broad range of training in our team, including EEG data recording and processing, statistical analysis, machine learning on biological data, ethics in research and data management, as well as scientific writing and presentation. You also have the opportunity to gain unique skills on commercialising biomedical devices.

Atomic Force Microscopy (AFM) is a powerful technique to determine the morphology of complex nanoscale assemblies, such as mRNA therapeutics used in the fight against COVID-19. It therefore has huge potential to transform materials characterization across sectors from medicine to manufacturing, providing rapid feedback into the efficiency of their manufacturing processes. AFM is limited, however, by its analysis tools, with the majority still carried out by hand, relying on highly trained and experienced researchers. The lack of automated analysis tools for the field is the rate limiting step for the use of AFM in translational settings including in the development of novel therapeutics. We have developed a transformative, rapid AFM analysis pipeline, combining the state-of-the-art AFM, carried out in the Henry Royce Nanocharacterization Laboratory and our open-source analytical AFM tool TopoStats, which automates identification and characterisation of molecules such as mRNAs within AFM images (www.github.com/AFM-SPM/TopoStats). This project will determine the shape, morphology and homogeneity of DNA, proteins and novel therapeutics in solution and correlate these with measurements of living cells.