Phenotypomics: Cellular sensors for chemical and biological hazard identification

Description

The project will utilise high end confocal microscopy in conjunction with data science and machine learning techniques to determine whether there are characteristic cellular morphological/physical (phenotype) changes in response to different types of biological and chemical hazards. The project builds on a very successful collaboration between the University of Liverpool and the UK’s Defence Science Technology Laboratory (Dstl). The project will be mainly based at the University of Liverpool in the laboratory of Prof. Julian Hiscox but will involve short research visits to Dstl. Prof. Hiscox’s laboratory focuses on emerging viruses (SARS-CoV-2, MERS-CoV and Ebola virus) and biological and chemical threat agents. This includes work on viral evolution, the host response and predicting the outcome of infection/challenge using AI/machine learning approaches. His laboratory is currently composed of 6 post-doctoral research scientists and 10 PhD students and presents a thriving research environment to lead on cutting edge science.

Prof. Hiscox has supervised 33 PhD students through to completion. He and Prof. Clark work extensively together as evidenced by recent publications from joint PhD students around ricin and chlorine exposure. Prof. Hiscox has obtained funding for a super high resolution confocal microscope to be used in Containment Level 3 (CL3) and this equipment has become fully operational. The project will involve infecting cells and potential model organs with viruses such as SARS-CoV-2 and imaging morphological changes using the confocal microscope. These will be compared to changes in morphology when these cells are exposed to other threat agents. Whilst the microscope has inbuilt AI the project will work with colleagues in mathematics to develop models of exposure and identify common and unique patterns.

Given the demands of the project, candidates should be familiar with cell biology and ideally imaging and unafraid of mathematics and data science. If you apply for this position, please e-mail your application to Ms Jill Hudson (jhudson@liverpool.ac.uk). Please submit a single application as pdf with the first page as a covering letter outlining your motivation and experience so far and the second page for a brief CV. This should be on A4 with Ariel Font 11 or 12 with fully justified margins. For the subject heading in your email please use ‘Dstl PhD 2024'. Any applications not following these instructions will be ignored. Candidates must have a primary degree at 2.1 level (or higher) and be UK national/resident.