Phenotypomics: Using AI to dissect the shape of cells in response to biological and chemical threat agents for hazard identification

The project will be aligned with the EPSRC Centre for Doctoral Training CDT in Distributed Algorithms: The What, How and where of Next-Generation Data Science.

The student will benefit from the cohort-based training associated with the CDT as well as access to the CDT’s dedicated supercomputing facilities at the University of Liverpool.

The CDT is part of the wider Signal Processing Group  where the student will benefit from collaborating with experts in Bayesian computational methodology, autonomy, decision support, data fusion, tracking, image processing, radar processing, acoustic analysis, text analytics, machine learning, behavioural analytics, simulation and energy-efficient hardware implementation.

Description: The project will utilise high resolution images of cells that have been exposed to a variety of biological and chemical threat agents. These include lethal viruses such as MERS-coronavirus, SARS-CoV-2 and chemical and biological agents such as chlorine and ricin. The focus of the project is to identify whether there are characteristic cellular morphological (shape) changes in response to these agents.

Our bodies are made up of millions of cells that have different functions, whether these be in the respiratory system, liver, neurological/brain functions etc. The functioning of these cells can be perturbed and destroyed by either virus infection or exposure to toxic agents. This can result in debilitating disease and death or long-term health consequences.

One of the complications in exposure to threat agents is identification of the agent itself (attribution), understanding the mechanism of action and mounting effective medical countermeasures. These factors underscore the philosophy of the PhD project.

We would like the student to use AI and machine learning approaches to understand how cells change shape in response to a threat agent and align this with high resolution data of host transcriptome and proteome changes. This will build a complex picture of function and mechanism.

We want to determine if different threat agents alter the morphology and functioning of cells in different ways and how this information can be used to influence attribution and treatment.

The project is a collaboration between the University of Liverpool and the Defence Science Technology Laboratory (DSTL).

The University of Liverpool principal supervisor is Prof. Julian A. Hiscox. He is Chair in Infection and Global Health. His laboratory is currently composed of 7 post-doctoral research scientists and 4 PhD students and presents a thriving research environment to lead on cutting edge science. The group works on viral, biological and chemical threat agents, many in partnership with DSTL. He has taken 37 PhD students through to successful completion of their studies.

The University co-supervisor is Prof. Simon Maskell who is Professor of Autonomous Systems Electrical Engineering and Electronics. His work sits at the interface between computer science, engineering and statistics.

This is a 4 year fully funded PhD studentship starting 1 Oct 2024. The successful student will receive funding for the UK tuition fees and a monthly maintenance at the UKRI Doctoral Stipend rate (£19,237 minimum per annum, 2024/25 rate which will increase on an annual basis). In addition to fees and stipend, the student will receive a training grant for research-related expenses such as training and conferences. 

The project is suited to a candidate with an undergraduate or master’s degree in a numerate subject, with either a computational and/or a mathematical background in programming and familiarity with the analysis of large datasets using AI/ML approaches.

This studentship is open to UK applicants only.

Apply now: https://www.liverpool.ac.uk/distributed-algorithms-cdt/apply/

Applicants please note: You must not submit a research proposal. The PhD project is defined. You must provide a supporting statement (no more than 700 words) that explains why you are interested in undertaking a PhD, this specific topic and joining the research group. More application guidance can be found on the apply link above.

Please note applicants will be dealt with on a first come first served basis, should we receive enough suitable applications we may close the advert early.