PhD opportunities

Muscle function is essential for healthy ageing, yet we know deficits in muscle stem cell (muSC) function occur when we get older, resulting in weakness and frailty. Increasingly, ageing research is looking to find novel and effective interventions to enhance muSC function in vivo and so promote a better regenerative response to injury and impaired muscle function. The challenge is how to identify such factors in the complex environment of the muscle, in which many cell types are important for controlling muSC function. By understanding how different cells communicate in regenerating muscle and how these affect the ability of the muSC to migrate to damaged muscle and effectively replace damaged myofibres forms the basis for this project.

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There are three unique features common to all vertebrates: the vertebral column, the brain as part of the nervous system and a complex head where sense organs became concentrated.

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One of the key questions in biology is to uncover how cells with the same genomic information become different from each other. This is not only important to understand embryo development, but also to determine what goes wrong in disease, how we can use this information to promote tissues regeneration or to reprogram cells for stem cell-based therapies.

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Hearing as one of the five human senses plays a crucial role in our quality of life and integration into society, impacting on speech and language skills. Congenital hearing loss has been estimated to occur at an incidence of 1 in 1000 births, and as such has a major impact on the life of many children.

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Haemoglobinopathies, including sickle cell disease and β-thalassaemia, are caused by mutations in the β-globin gene and remain a major global health burden. While genome editing offers curative potential, current delivery approaches rely heavily on viral vectors or electroporation, which can compromise haematopoietic stem cell (HSC) viability and long-term engraftment. This project will develop a nanoneedle-based platform for the precise, non-viral delivery of gene editing tools (e.g. CRISPR-Cas9 or base editors) into primary human HSCs.

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Ageing involves a number of molecular changes across all cell types, resulting in altered cell-cell communication, function and maintenance of tissues. To identify potential causal factors driving age-associated tissue dysfunction an accelerated ageing animal model will be used to explore how defined perturbations affect cells and molecules. By modelling the impact of these changes it is then possible to predict potential causal factors driving aberrant cell function and hence tissue homeostasis in ageing. This project will use zebrafish telomerase mutants showing accelerated ageing to examine the importance of ageing-associated factors in regulating muscle homeostasis and repair. Using data from live imaging, immunolabelling and transcriptional profiling computational models will be generated to make predictions about ageing-associated changes and tested by experimental manipulation. The key goal of the project is to build a digital twin for molecular mechanisms underpinning muscle ageing which will be validated with in vivo experiments.

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This PhD project proposes a non-invasive diagnostic platform using Raman spectroscopy to identify early biochemical signatures of cancer in saliva, with a focus on oral cancer and broader systemic malignancies such as lung cancer and breast cancer. By integrating surface-enhanced Raman spectroscopy (SERS) with advanced machine learning techniques, the study aims to detect subtle molecular changes, including altered protein structures, nucleic acid content, and metabolic byproducts associated with tumour development. Saliva samples will be collected from both healthy individuals and diagnosed patients, followed by spectral acquisition and data-driven classification modelling. This approach offers a rapid, cost-effective, and easily deployable alternative to conventional screening methods, which are often invasive or resource-intensive. The ultimate goal is to develop a portable diagnostic tool capable of early-stage cancer detection, improving survival rates and enabling widespread screening in both clinical and low-resource settings.

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Stem cells are critical for maintaining organ function, but their function is impaired in ageing and disease. Although many factors have been observed to change in ageing it is unclear what the causal factors driving changes to function are. Using zebrafish telomerase mutants, a powerful genetic model of ageing, we aim to investigate how specific molecular changes in ageing affect stem cell behaviour in muscle in vivo. Live cell and functional imaging modalities will be deployed to measure stem cell behaviour and profile molecular composition of tissues. Specifically, fluorescently labelled cells will be visualised relative to biosensors for metabolism and inflammation in conjunction with Raman and DESI-MS imaging of tissue molecular composition. Using AI methods for embedding multimodal data we will make causal predictions about functionally important changes to stem cell environment and test these in vivo using genomic manipulations.

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Glioblastoma multiforme (GBM) is characterised by profound spatial heterogeneity and rapid adaptation to therapy, which underpin treatment resistance and poor patient outcomes. Current spatial transcriptomics approaches provide high-resolution molecular maps but are inherently destructive, preventing longitudinal analysis of the same tissue. This project will develop Dynamic Spatial Transcriptomics (DyST), a nanoneedle-enabled platform for minimally perturbative, repeated RNA sampling from living tissues. By integrating nanoneedle arrays with spatial barcoding technologies and multiplexed imaging, the student will establish a workflow to capture gene expression dynamics in tumour-bearing mouse brain slices undergoing temozolomide treatment. The platform will enable direct observation of spatiotemporal transcriptional changes and cell–cell interactions within intact tumour microenvironments. Applying DyST to GBM will uncover the molecular trajectories and spatial niches associated with the emergence of treatment resistance, establishing a broadly applicable framework for dynamic spatial biology.

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Periodontitis is a global disease burden, underpinned by chronic inflammation, driven by microbial dysbiosis and an exaggerated host immune response, leading to irreversible destruction of the supporting structures of the teeth. Emerging evidence have revealed that features consistent with trained immunity are present in periodontitis, including hyper-responsive circulating monocytes and altered neutrophil behaviour. In particular, monocyte-derived macrophages display a heightened inflammatory state, contributing to excessive cytokine release, oxidative damage, and osteoclast-mediated bone loss, yet the mechanisms sustaining this hyper-inflammatory phenotype remain poorly defined. Vitamin D deficiency is associated with increased severity of periodontal disease and increased tooth loss. Vitamin D is a potent immunomodulator that regulates epigenetic and metabolic pathways central to trained immunity; however, its impact on macrophage-trained immunity and its ability to reverse maladaptive immune memory remain unclear. This PhD project will test the central hypothesis that vitamin D modulates trained immunity in macrophages by reshaping their epigenetic and metabolic programming, thereby restoring a balanced inflammatory and antibacterial phenotype and enabling the therapeutic reversal of established trained immunity and periodontitis-induced alveolar bone loss.

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This PhD will build on previous pioneering studies from our group, which have shown the clinical results of minimally-invasive non-surgical therapy (MINST) and how they are mediated by molecular changes in inflammatory and repair biomarkers.

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This project addresses the urgent need for novel and effective therapeutic strategies for the management of peri-implantitis, a highly prevalent condition associated with severe biological and functional consequences if left untreated. The European Federation of Periodontology (EFP) S3 guidelines highlight the current lack of clear, reliable, and predictable treatment protocols for managing peri-implant defects. Recent randomised controlled trials indicate that the adjunctive use of bone grafts does not confer a clear advantage over open flap debridement (OFD) alone in peri-implantitis therapy. Platelet-rich fibrin (PRF), an autologous platelet concentrate, offers multiple biological advantages, including the promotion of tissue regeneration and wound healing. The PhD study will test the hypothesis that the use of PRF in the surgical management of peri-implantitis will improve outcomes at 12 months.

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The majority of emerging infectious diseases in humans over the past few decades have resulted from the interspecies transmission of RNA viruses. The PhD will use computational tools and machine learning techniques to predict how these viruses infect cells in the human upper respiratory tract and avoid the host immune response.

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The project aims to explore the intricate relationship between the human microbiome and Alzheimer's Disease (AD). Despite increasing evidence suggesting a link between the microbiome and AD, the detailed molecular interactions remain largely unexplored. This study seeks to bridge this knowledge gap by employing a multi-omics approach, integrating genomics, transcriptomics, proteomics, and metabolomics to comprehensively analyze these interactions.

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This project explores the host-microbiome interactions in periodontal and peri-implant diseases, and possibilities of microbial virulence factors modulation and fine-tuning of the host immune responses for periodontal and peri-implant diseases risk stratification, early detection, precision and advanced treatment and for monitoring treatment outcomes.

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Rapid evolution of seasonal influenza virus continues to undermine vaccination efforts. Traditional phylogenetic and serological methods remain reactive and limited in predictive power. Despite decades of surveillance, vaccine mismatch continues to occur, with significant public health and economic consequences. This project proposes the development of a AI-driven framework that uses generative AI capable to predicting virus–antibody interactions. Integration of viral sequence data, structural modelling, and immunological data to predict antigenic properties of novel viruses and identify escape-prone mutations.

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Extracellular vesicles (EVs) derived from periodontal cells have emerged as promising cell-free therapeutic agents, owing to their ability to mediate intercellular communication and modulate key regenerative processes. This project aims to investigate the targeted modulation of periodontal cell-derived EVs to enhance their therapeutic potential for wound healing and tissue regeneration. The study will focus on optimising EV bioactivity through manipulation of in vitro periodontal cell conditions, including inflammatory priming and environmental cues, to enrich regenerative microRNAs and proteins. Comprehensive characterisation of EVs will be performed to assess their molecular composition and functional properties. The effects of modified EVs on key biological processes—such as cell proliferation, migration, angiogenesis, and immunomodulation—will be evaluated using in vitro assays. By elucidating mechanisms underpinning EV-mediated repair and developing strategies to enhance their functionality, this project seeks to establish a novel, scalable, and clinically translatable approach for periodontal regeneration. This work has the potential to advance precision regenerative therapies and provide a safer alternative to conventional cell-based interventions.

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This PhD project will build on previous studies from our group, which have shown novel characteristics of intra-surgical periodontal wound granulation tissue and its association with periodontal wound healing. This project will involve clinical sample processing of periodontal granulation tissue derived from periodontal surgical procedures for high resolution cellular and molecular analysis, and to correlate finding with clinical outcomes of surgical procedures.

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There is wide recognition that dentistry is changing and that the skills and knowledge needed by people training to be dentists is also changing. A growing body of researchers are now looking at workforce planning and shaping the workforce of the future with a focus on the mix of skills and professions needed within the dental team (see for example Wanyonyi et al. 2014). There are also plenty of papers looking at the different ways in which dental students can best be taught the skills needed (see for example: Fincher & Shuker 2001, McAndrew et al. 2015; Reissman et al. 2015) at who they should be taught with (Nadershahi et al. 2012) and at professionalism within dentistry (see for example Trathen & Gallagher 2009). But pedagogical concerns and workforce planning are only part of the picture. The focus on providing patient centred dental care, coupled with an increased emphasis on prevention and widening understanding of the unequal distribution of oral health problems and related risk behaviours, all pose challenges to traditional paternalistic and/or business oriented models of dental care and require a change in mindset alongside the changing skillset.

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The aim of this research project is to explore structural ableism in dentistry, not just as a form of discrimination that disabled people face within the healthcare sector but as a form of discrimination that is embedded in the structures, organisations and institutions that make up the healthcare system itself.

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Oral health is increasingly recognized as a critical yet often overlooked component of general health, particularly in the context of multiple long-term conditions (MLTCs). A growing body of epidemiological and clinical evidence has established associations between oral diseases—especially periodontal disease—and systemic conditions such as diabetes, cardiovascular disease, rheumatoid arthritis, respiratory disorders, cognitive decline, and mental health issues. These relationships are often bidirectional and mediated by shared risk factors including chronic inflammation, lifestyle behaviours, socioeconomic determinants, and health service access barriers. While cross-sectional and longitudinal studies have consistently reported correlations between poor oral health and the presence or progression of MLTCs, the underlying mechanisms remain poorly understood. Furthermore, current health systems and disease management strategies largely treat oral health in isolation from other chronic conditions, despite clear indications that integrated care could improve outcomes and reduce healthcare burden. A number of critical research gaps persist. First, there is limited use of longitudinal, multimodal data to unravel the temporal sequencing and potential causal pathways linking oral health with MLTC trajectories. Second, most existing studies are condition-specific rather than addressing the multimorbidity cluster patterns that are increasingly common, particularly in aging and socioeconomically disadvantaged populations. Third, there is a scarcity of implementation-focused research exploring how oral health can be embedded into chronic disease prevention and care models, especially within primary care and public health settings. This PhD project proposes to address these gaps by investigating the role of oral health in the development, clustering, and progression of MLTCs using large-scale population-based datasets and electronic health record (EHR) from King’s local database, UK national data, and international data. The project will employ advanced statistical and epidemiological techniques—such as survival analysis, latent class trajectory modelling, and Mendelian randomization—to examine potential causal pathways and inform the design of integrated, patient-centred approaches to chronic disease prevention. Ultimately, the findings will have significant implications for policy, clinical practice, and interdisciplinary public health strategy.

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Apical periodontitis (AP) is a chronic inflammatory disease of the tissues surrounding the tooth root apex, caused by persistent infection within the root canal system. Our previous studies have characterised the microbiome of primary and secondary endodontic infections using targeted gene sequencing, identifying key cultivable pathogens associated with treatment failure. When conventional root canal therapy is unsuccessful, apical microsurgery provides a minimally invasive surgical option to remove infected tissue and promote healing. Platelet-rich fibrin (PRF), an autologous biomaterial rich in platelets, growth factors, and stem cells, has shown promise in enhancing tissue regeneration. This PhD project will evaluate whether PRF use in apical microsurgery improves healing, bone regeneration, and clinical outcomes. A randomised controlled design will integrate clinical, microbiological, and inflammatory analyses to inform evidence-based regenerative strategies.

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Head and Neck Cancers are a heterogenous group of cancers with origins/locations in more than 30 sites in the head and neck region. Tongue cancer is the most common location for head and neck cancer in the UK and recent population‑based data have documented rising incidence among younger and middle‑aged adults. Treatment involves surgery, radiotherapy, chemotherapy, immunotherapy or a combination of these modalities, with surgical resection of the tongue remaining the most common first line of treatment. Treatment for tongue cancer is intensive, traumatic and can result in profound changes to the body. It may lead to functional impairments relating to swallowing, mobility, chewing, speaking and eating. Patients may also need to adapt to physical changes and disfigurements which can be psychologically distressing and have a negative impact on their well-being. While there is a broad literature exploring HNC as a group there is less known about tongue cancer and about patient experiences, particularly in the longer term. The aim of this project is to fill this gap, looking at the experiences of people with a diagnosis of tongue cancer from diagnosis onwards with a focus on longer term impacts. This is a qualitative project.

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We are recruiting for a PhD candidate to join our team working at the forefront of large language models (LLMs), diffusion models, and generative AI. The successful candidate will develop novel architectures, training strategies, and evaluation frameworks for foundation models, with applications spanning but not restricted to scientific and biomedical domains. You will explore cutting-edge topics such as multimodal learning, controllable generation, reasoning, efficient fine-tuning and inference. The research may involve building and analysing generative models for structured data, text, images, or scientific sequences, as well as investigating how these models can support discovery and decision-making in complex, high-stakes environments. As part of our broader research portfolio, you will also have the opportunity to contribute to an interdisciplinary Horizon Europe project focused on AI-driven biomanufacturing. This offers a unique setting to translate generative AI methods into real-world scientific workflows, working alongside biologists, clinicians, and engineers. Domain knowledge of such specific applications isn’t mandatory.

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Non-technical skills, including communication, professionalism, situational awareness, and teamwork, are central to safe and effective clinical practice. However, students’ training, development and assessment in these domains remain challenging, often relying on subjective, episodic observation that limits consistency and scalability. This PhD project aims to design and evaluate an artificial intelligence AI-driven framework to enhance the assessment and training of human factors in clinical education. Grounded in Situated Learning Theory and Self-Regulated Learning (SRL), the project reconceptualises learner performance not as isolated skills, but as participation in socially and contextually embedded clinical interactions. Using recordings of authentic and simulated clinical encounters, the research will apply machine learning (ML) and natural language processing (NLP) to analyse how learners engage in clinical practice. The system will identify behavioural markers such as turn-taking, responsiveness to patient concerns, role positioning, and teamwork dynamics. In doing so, it will determine where learners sit along a continuum from peripheral participation (novice) to full participation (competent practitioner). Building on this situated perspective, the project will develop AI-generated feedback designed to support self-regulated learning. By making behavioural patterns visible, the system will enable learners to monitor, evaluate, and adapt their performance. Feedback will be aligned with the SRL cycle (supporting planning - forethought, performance monitoring, and reflective evaluation) thereby promoting deeper metacognitive awareness and sustained improvement. A mixed-methods approach will be used to validate AI outputs quantitatively and explore learner and educator experiences qualitatively. The research will also examine how AI-informed feedback can enable adaptive, personalised learning pathways and inform targeted simulation-based interventions. Ethical considerations, including data privacy, bias, transparency, and responsible AI deployment, will be integral to the study design. This project offers a novel contribution by integrating context-sensitive analysis of clinical participation (Situated Learning) with AI-supported metacognitive development (Self-Regulated Learning). The outcomes will inform the design of intelligent educational systems with implications for clinical training, simulation, and patient safety.

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Haptic simulation technologies are increasingly embedded within dental education, offering immersive environments that combine visualisation, tactile feedback, and interactive features to support early skill acquisition. However, these systems introduce novel affordances—such as unrestricted zoom, enhanced visual clarity, altered colour contrast, absence of patient interaction, lack of water spray, and reduced reliance on finger rests—that may encourage non-authentic behaviours unlikely to transfer to real clinical settings. This PhD project will investigate how such affordances shape learner behaviour and, critically, how they may promote negative transfer when students transition to phantom head or live patient care. Drawing on theories of transfer of learning, ecological validity, and affordances and constraints, the research will systematically identify discrepancies between simulated and real-world practice, including perceptual, motor, and cognitive differences. Using a mixed-methods design, the project will examine how learners engage with tactile and visual cues across environments, and will identify simulation-enabled strategies that deviate from authentic clinical practice. Based on these findings, the candidate will design and implement pedagogically constrained interventions—such as limiting visual augmentation (e.g., zoom), reintroducing procedural realism (e.g., simulated water spray), scaffolded reduction of system support, and explicit metacognitive instruction regarding appropriate tool use. The effectiveness of these design interventions will be evaluated in relation to students' performance, clinical reasoning, and skill transfer to more authentic settings. The project will generate evidence-based design principles and teaching strategies to guide educators, curriculum developers, and simulation technology designers in optimising haptic simulation for meaningful learning transfer.

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The rapid expansion of remote and blended learning in higher education has transformed the delivery of clinical training in dentistry and oral health. Clinical video resources are increasingly used to support the teaching of complex procedures; however, their pedagogical effectiveness, optimal design, and impact on student learning remain insufficiently understood. This PhD project will investigate how clinical videos can be systematically designed and implemented to enhance learning in dental education. The research will adopt a mixed-methods approach to develop and evaluate a suite of high-quality clinical video resources tailored to remote learning contexts. It will explore key instructional design features such as visualisation, narration, segmentation, and interactivity that support the acquisition of procedural knowledge and clinical reasoning. The project will further evaluate the effectiveness of clinical videos in improving student understanding, skill development, and learning outcomes, comparing their impact with traditional face-to-face teaching approaches. In addition, the research will examine the experiences and perceptions of students and educators and assess the scalability and cost-effectiveness of video-based learning in dental curricula. The findings will inform evidence-based guidelines for best practice in clinical video design and contribute to the advancement of digital and inclusive dental education.

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