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Queen’s Doctoral Training Programme - Multi-dimensional approaches to understanding microbe/host interactions in the context of disease, therapeutics and community resilience

Queen’s Doctoral Training Programme - Multi-dimensional approaches to understanding microbe/host interactions in the context of disease, therapeutics and community resilience

Hosts, pathogens and environment

Funding Duration: 3.5 years

The vision of this DTP is to address global challenges, from climate change to antimicrobial resistance, by understanding the complex interactions between microbes and the environment, how pathogens evolved within the host to evade the immune system and counteract our therapeutic interventions.

Our research models will exploit:

  1. big-data analytics combined with machine learning to model and predict how perturbation in microbial community structure/function affects, e.g. host phenotypes, host/microbe interactions, disease onset/recovery, bioprocess efficiency and ecosystem services and;
  2. dissect the interactions within hosts employing the most innovative single cell analysis (dual RNA seq, proteomics, live imagining) and models (from knockout and knockin mice to insects).

The proposed training programme will offer an opportunity for doctoral researchers to study within a multidisciplinary setting and undertake integrated training in the emerging field of microbiome enabled machine deep learning analysis, and the well-established fields of microbe/host interactions, immunology and translational research.

Queen’s Doctoral Training Programme - Multi-dimensional approaches to understanding microbe/host interactions in the context of disease, therapeutics and community resilience

The assembled supervisory group brings together experts from multiple, distinct fields of research, including immunology, machine learning and data science, environmental, animal and clinical microbiology, bioinformatics, genomics, biochemistry, ecology and systems biology. In addition to laboratory and subject-specific expertise, students will acquire a range of personal and professional skills such as independence, creativity, persistence, research translation, communication and collaboration which can be tailored to suit their career aspirations. The combination of disciplines brought together by this application will equip those PhD students involved in the programme with an interdisciplinary and innovative mind-set, and the capacity to transfer their skills to other scientific fields.

Researchers will benefit from access to diverse, cutting-edge facilities, for example, bioimaging, single cell genomics, big data interrogation and management platforms. On top of our ambition to deliver a world-class research agenda, it is an essential part of our ethos to drive innovation and contribute to the prosperity of our community. We plan to offer designated training modules in entrepreneurship and innovation as well as scientific outreach. Moreover, we recognize that a smooth transition into a PhD training programme has a significant impact on student wellbeing and development. Therefore, we will also offer specific support to students during the initial months of the programme, including a buddy system and appropriate signposting towards Queen’s Graduate School activities to maximize synergies.

With those skills developed during this programme, students will be prepared to enter the job market and pursue different career paths, as e.g. researchers, educators, data analysts, policy makers or entrepreneurs. As such the researchers so trained will be on a trajectory to become future leaders in the integrated, societally relevant, application of data to disease pathogenesis, health and well-being, food and agriculture, environmental impact and healthcare.

The closing date for applications is midnight on 31 March, 2020.

Queen’s Doctoral Training Programme - Multi-dimensional approaches to understanding microbe/host interactions in the context of disease, therapeutics and community resilience
Furthering our understanding of microbial community dynamics through antimicrobial-driven dysbiosis(Project fully funded for 3.5 years)Details
Investigation of the limits of niche specialisation and plasticity in the rumen microbiome(Project fully funded for 3.5 years)Details
Modelling and optimising microbial nutrient cycling for the promotion of a circular bioeconomy(Project fully funded for 3.5 years)Details
Macrophage-bacteria interactions in cystic fibrosis: the cellular microbiology of the novel opportunistic bacterium Achromobacter speciesDetails
Deep learning analysis of large-scale sequence data in health and diseaseDetails
Determining the microbiome of the homes of people with chronic respiratory diseasesDetails
Development and testing of deep learning algorithms to predict pulmonary exacerbations and development of antimicrobial resistance in people with chronic respiratory diseaseDetails

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