Below is a list of Studentship Opportunities including the associated supervisors, project titles and further information.

This project is an exciting collaboration between King’s College London and Public Health England. Here, we will determine if short and long-lived biomarkers of radiation exposure are present in blood samples of two cohorts of radiation exposed workers: (i) operators performing X-ray guided procedures and (ii) technologists who work in Nuclear Medicine, who administer radioactive injectable therapies to cancer patients. The biomarkers will include those based on DNA damage (e.g. yH2AX) and chromosome mutations (e.g. dicentrics) as well as transcriptional markers of gene expression, which are known to provide information about radiation dose and exposure conditions as well as risk of future health effects. This work will help our understanding of how work-related radiation exposures in nuclear medicine and cardiovascular surgery might be linked to longer term effects, with the aim of ensuring appropriate protection of radiation workers and the wider population from ionising radiation.
This project will utilize biomarker and questionnaire data from the BEED cohort of pregnant women living around municipal waste incinerators across the UK, in order to investigate the potential impacts of the environment on breast milk and subsequent early life development. The project includes epidemiological and metabolomic data analyses but also a wave of data collection from the children of the BEED women.
Identification and monitoring of potentially hundreds of new highly toxic agents (HTAs) in drinking water remains a significant analytical challenge. This project will characterise and classify new chemical HTAs in UK municipal, well and bottled drinking waters and assess their potential risks for human exposure. Objectives: i) to develop AI-assisted high resolution analytical methods for suspect screening of large numbers of neurotoxins and HTA markers including pharmaceuticals, pesticides, organometallics, plastic-related compounds, disinfectant by products; ii) to conduct a temporal survey of drinking water sources across the UK; iii) analyse urine samples to assess individual-level exposure and analyse wastewater from the corresponding city to extrapolate to both human population and environmental exposure; iv) evaluate public health and environmental risk of HTAs from drinking water sources.
Exposure to metals via inhalation is a relatively new and important area of research given the potential for neurological effects within an ageing population. This project aims to combine recent advances in measurement of airborne metals in different urban settings (indoor, outdoor, transport) with highly detailed population exposure modelling, to provide new insight into individual exposure, representative of the entire London population. We will estimate the important sources of toxic metals, assess policies for exposure reduction and provide data for ongoing health research.
Particulate matter air pollution (PM) detrimentally affects lung development and function. This project will investigate real-world PM samples, including different biodiesel exhaust materials, that drive inappropriate inflammatory responses (e.g. in bronchial epithelial cells, neutrophils, dendritic cells, monocytes/macrophages), to link this with PM compositional data in order to identify relevant signalling pathways and explore mediators that may mitigate detrimental PM effects. Studies in patient cohorts (e.g. asthma, COPD) will test the role of the pathways identified in comparison to matched healthy subjects
Separating short-term health effects of nitrogen dioxide (NO2) and fine particulate matter (PM2.5) is important for policy. The student will study whether (i) the relative risk for NO2 in time-series analysis reduces as the NO2/PM2.5 ratio increases, with particle trap use, (suggesting an apparent effect due to PM2.5 rather than NO2); (ii) controlling the NO2 effect for PM2.5 actually controls more for secondary than primary PM and (iii) causal inference methods provide further insight.
Co-use of fentanyls with heroin substantially increases risk of overdose and death. Their potential use in a CBRN attack also requires emergency preparedness capacity including detection methods for a suite of fentanyls and urinary metabolites. The establish a mechanism for the monitoring the use of fentanyls in the UK and establish preparedness for monitoring of potential CBRN incident involving fentanyls. Objectives: i) Literature review of the known fentanyl usage trends; ii) charactering fentanyl metabolism using liver microsomes and AI-assisted liquid chromatography-high resolution mass spectrometry (LC-HRMS); iii) Quantitative analytical method development and validation using rapid LC-tandem mass spectrometry; iv) Analysis of human urine samples and a UK-wide wastewater pilot study to estimate national fentanyl usage.
There is a pressing need for (i) a high quality, up to date systematic review/meta-analysis on air pollution and pre-term birth to generate concentration-response functions for policy analysis. The student can then develop the project in various directions, such as undertaking (i) a health impact assessment, (ii) stakeholder engagement to improve public understanding/develop interventions to reduce pregnant women’s exposure, or iii) optimisation of the systematic review process testing methods such as artificial intelligence/machine learning using the literature dataset from (i).
The incidence of male and female infertility has increased in recent years. Advanced maternal age is known to be the leading factor responsible but other factors that affect both men and women including air pollution, may contribute. Epidemiological evidence linking exposure to ambient air pollution with fertility disorders in women and men (e.g. reduced fecundity as measured by time to pregnancy) is still inconsistent with many study limitations. An opportunity to address this topic is available by using UK COSMOS, an ongoing large cohort study that provides information on many potential confounding variables. First, an overall design and model will be developed, based on available air pollutant information and verified across England. Second, exposure to ambient air pollutants such as NO2, NOX, PM2.5 and PM10 will be attributed to each participant by using residential history, retrospectively collected through questionnaires. Third, the association between exposure to ambient air pollution and reduced fecundity will be evaluated.
Perfluorinated chemicals include a large number of synthetic compounds which are bioaccumulative and very persistent in the environment and in the human body. Despite their common use in a wide range of daily products, a recent briefing by the European Environment Agency highlighted that risks to human health and the environment were still poorly understood. Some studies have suggested an impact on infant birth weights, the immune system, cancer and Covid19. The multi-disciplinary project, conducted in close partnership with Public Health England, will involve a wide range of techniques from reviewing current evidence, to measurements in the environment using high-resolution monitoring equipment and epidemiological analysis at population level.
New material types (advanced polymers, fibres, composites, nanomaterials etc.) are constantly being developed and applied in products with the potential for human exposure. Assessing their potential toxicity using human relevant models on an appropriate timescale for efficient regulatory control is an ever-growing issue. To address this challenge, we will use our advanced aerosol-exposure air-liquid-interface systems to mimic realistic human exposure scenarios and ‘omics and other analytical techniques to understand their biological responses. Recent studies have highlighted the importance of macrophages and, in particular, macrophage autophagy as important in this context so the focus will be on macrophage and other immune cell reactions and interactions and how these are modified by material properties and pre-existing disease conditions.
Indoor exposure to chemicals contributes significantly to personal exposure in developed countries, where people spend up to 90% of their time in the indoor environment. Previous studies have identified volatile and semi volatile organic compounds ((S)VOCs) in the indoor environment, often associated with suspended particulate matter. SVOCs are of particular public health concern, however, the lack of documented indoor emission properties for many chemicals as well as the limited data on human exposure routes lead to uncertainties in estimating the associated health risk. Additionally, experimental data for validation are often scarce or absent. The overall objective of this project is to further develop and validate a microenvironmental exposure model to incorporate predictions of indoor concentrations of SVOCs and other pollutants in the home environment. The student will extend an existing integrated exposure model for additional indoor air pollutants and carry out fieldwork to evaluate the performance of the model. The enhanced model will then be applied to cohorts or populations to investigate links between residential exposure to pollutants and health outcomes, utilising existing database resources and bespoke field campaigns.
Particle air pollution is a major health concern in the UK and has been linked to around 29,000 attributable deaths annually. Home burning of solid fuel (wood and coal) is estimated to produce around 39% of the particle pollution (PM2.5) emitted in the UK, greater than that from road transport. Local air pollution hotspots have been identified by the local air quality management process in the UK but this has focused mainly on major roads, enclosed streets and busy road junctions. Localised emissions from solid fuel burning are not well captured in existing emission inventories and current measurement networks. A better understanding of solid fuel exposure hotspots will be necessary to enable a focus on interventions. The PhD project will: • Investigate measurement methods of wood burning PM. • Devise and pilot street-by-street mapping of solid fuel PM concentrations and compare these with datasets from national fixed measurement locations. • Undertake longer term measurements of wood burning PM outside and inside UK homes • Determine the range of PM exposures that might be expected from wood burning in UK urban areas.
Aim: To determine whether long-term exposures to urban air pollution is associated with evidence of accelerated aging and chronic disease risk. Ageing is one of the principal risk factors for non-communicable disease, however the rate of age-related declines in physiologic function and immunity varies considerably between individuals. It has been proposed that DNA methylation age biomarkers may be good predictors of age-related diseases and mortality risk and therefore in this study we wish to examine whether long term exposure to urban air pollution, particularly that derived from traffic emissions promote these age-related changes, after adjustment for other lifestyle factors. In addition, we wish to examine how these epigenetic markers relate to other measures of biological age, including telomere shortening and changes in the metabolome.
General aims of the project include: 1) Investigate socioeconomic determinants of epigenetic aging in children form the HELIX, ALSPAC and Generation 21 cohorts, 2) Develop novel multi-omic biological age (development ) assessment in HELIX and ALSPAC children using epigenetics, transcriptomics, metabolomics and proteomics and developmental endpoints (cognition, growth, adiposity) 3) Investigate role of physiological stress in biological aging, using glucocorticoid steroid profiling in HELIX children.
The environment contains a large number of chemicals whose potential toxicity for humans is unknown. We propose an agnostic approach based on metabolomics. The aim of the fellowship is to apply statistical and laboratory techniques of data mining for the detection of low levels of metabolites and xenobiotics in blood, to fill the knowledge gap in the understanding of the exposome. New analytical methods will consist in adding concentration steps prior to analysis, improving sensitivity and using state-of-the-art equipment. The candidate will then use in depth statistical analysis, multi-OMICs and network analysis to establish links between measured environmental exposures and selected disease outcomes. This will be done in several thousands of individuals over five cohorts (Exposomics project). We will evaluate the specificities of xenobiotic signatures, and the pathways they may affect to understand how exposures influence critical biochemical processes. These results will provide unprecedented information on xenobiotic exposures in humans.
It is widely recognised that air pollution is a public health concern accountable for numerous health problems and tens of thousands of premature deaths per year in the UK. Though air pollution is a problem faced by everybody, research has shown that those living in neighbourhoods of low social economic status (SES) are more likely to be exposed to higher air pollution levels. In addition, those with underlying health conditions, including diabetes, heart and lung disease are more susceptible to its health effects. This equity gap has recently become more apparent with the COVID-19 pandemic, as air pollution has been linked to poor health outcomes of COVID-19, with a disproportionately high impact within the Black, Asian and Minority Ethnic (BAME) population. The reasons for this are not yet known but are likely to relate to existing vulnerabilities including health complications, low SES, absence of choice and awareness. The very visible presence of face masks on our streets has made people think about what they breathe and the effect of what is in our air much more. Despite big differences in how each risk affects our health, this increased awareness has created perceived connections between COVID-19, air pollution and health. However, this connection is currently anecdotal and we do not have a good understanding of how the COVID-19 pandemic will influence people’s perceptions of air quality and health. This study aims to investigate vulnerable and minority ethnic groups’ perceptions of the relationship between air pollution and health and how this relationship has been impacted by the coronavirus (COVID-19) crisis. The empirical evidence gathered from this research has the potential to contribute to understandings on the acceptability, engagement and impact of current governmental and non-governmental efforts to address the air pollution problem.
Cardiometabolic risk factors and their subsequent conditions, including obesity, diabetes and cardiovascular disease, are major causes of multimorbidity in the UK population. The ongoing COVID-19 pandemic is likely to have strong indirect effects on the management, control, and outcomes of these conditions, and conversely, these conditions also represent high risk conditions for COVID19 and other infections. As chronic conditions are consistently associated with social deprivation and their outcomes are affected by access and continuity of care, healthy behaviours and supportive environments, the COVID-19 pandemic in the UK could have enormous variation in the levels of disruption and consequent health outcomes. The overall aim of this project is to quantify the indirect impact of COVID19 on chronic disease management, care, and outcomes and determine the area- and environmental-level risk variation and risk factors for adverse impact of COVID10 on chronic disease care and management. Specific aims in this work will be to: 1) Describe variation and map the levels of care, management, and disease related to cardiometabolic disease; 2) Examine the impact of COVID19 on the short-term and long-term impact of cardiometabolic risk factors and outcomes; 3) Identify and quantify the major environmental and socio-demographic risk factors for adverse impact of COVID on chronic disease management, care, and outcomes.
When assessing the effect of air pollution concentration on health outcomes, it is crucial to understand how the different pollutants acts and interact, however, this is challenging due to their high correlation. This project will compare and contrast different multi-pollutant methods to detect pollutant-specific effect on health. In particular, the student will consider (i) propensity score based methods, as a way of disentangling the effect of each pollutant, adjusting for the other ones and (ii) machine learning approaches, to estimate pollutant profiles. The modelling will be based on a Bayesian framework, to account for uncertainties throughout.
Ultrafine particles (UFP) are composite pollution measures (similar to larger size Particulate Matter); in order to understand their health effects, it is necessary to move from total UFPs back to their sources, to design and implement effective policies targeting the reduction of those sources showing evidence of negative health effects. The project looks at developing and applying machine learning tools to answer the question: which sources of UFPs around Gatwick airport are detrimental for the health of the local population? The project will entail statistical methodological developments in two phases: i) the student will work on non-parametric methods such as Dirichlet processes, or other machine learning approaches to apportion the UFPs to their sources; ii) then the student will develop a flexible regression model to link the apportioned sources and the health outcome(s) – both separately from the source apportionment or in a joint fashion, to allow uncertainty to fully propagate across the model components.
With the term big data analytics we refer to the collection, processing, and analysis of large sets of data with the aim of discover patterns and meaningful information. Processing and computing for such large volumes of (multidimensional) data raise, however, new challenging problems, which require scalable algorithms and creative statistical and probabilistic solutions. This project will focus on Earth observations and, in particular, on satellite-derived observations for mapping time-evolving and spatially-varying climate and environmental factors associated with mosquito-borne disease dynamics. Taking advantage of a longstanding relationship with the University of Sao Paulo (Brazil), the student (i) will extend and develop spatio-temporal geostatistics methods stemming from Bayesian inference to retrieve information from remote sensing measurements, then (ii) will investigates the synergistic effects of the local environmental and climate variations on mosquito-borne diseases in Brazil to support outbreak risk management, providing inputs for strengthen early warning systems.
Health surveillance is well established for infectious diseases, but less so for non-communicable diseases. A wide range of spatio-temporal methods are currently available, ranging from traditional tools such as SatScan and Cusum to more recent Bayesian approaches. Choices of methods are often based on easy of use, rather than on best performance and accuracy. The aim of this project would be i) to conduct a review of available methods, ii) to define the appropriate simulation framework to compare the different methods identified; and iii) to create guidance on the strengths and weaknesses of each method considered and on when to use each method.