Mathematics: UKRI CDT PhD Scholarship at Swansea: dynamical systems modelling of sepsis
- Full cost of tuition fees, plus a stipend
- 12 February 2021
Mathematics: UKRI CDT Scholarship in Artificial Intelligence, Machine Learning and Advanced Computing: ML-guided dynamical systems modelling of sepsis
This scholarship is funded by UK Research and Innovation (UKRI).
Start date: October 2021
The UK Research and Innovation (UKRI) Centre for Doctoral Training (CDT) in Artificial Intelligence, Machine Learning and Advanced Computing (AIMLAC) aims at forming the next generation of AI innovators across a broad range of STEMM disciplines. The CDT provides advanced multi-disciplinary training in an inclusive, caring and open environment that nurture each individual student to achieve their full potential. Applications are encouraged from candidates from a diverse background that can positively contribute to the future of our society.
Our doctoral training programme is constructed around three research themes:
- T1: data from large science facilities (particle physics, astronomy, cosmology)
- T2: biological, health and clinical sciences (medical imaging, electronic health records, bioinformatics)
- T3: novel mathematical, physical, and computer science approaches (data, hardware, software, algorithms)
- First supervisor: Dr Noemi Picco (Mathematics, Swansea University)
- Second supervisor: Dr Farzad Fathi Zadeh (Computer Science, Swansea University)
- Potential supervisor/Collaborators: Dr Thomas Woolley (Cardiff University) and Professor Peter Ghazal (Cardiff University)
Department/Institution: Mathematics / Swansea University
Research theme: T3: novel mathematical, physical and computer science approaches
Sepsis is defined as an abnormal and uncontrolled response of the immune system to infection. Because of our lack of understanding of why and how the immune response goes awry, at present there is no cure for sepsis, but only strategies to manage the symptoms. Critically, in many cases patients present with mild symptoms that very quickly degenerate into organ failure and life-threatening conditions, requiring resuscitation. It is therefore crucial that we understand the early onset of sepsis and recognise the patterns of drastic transition to severe condition.
A dynamical systems approach to the description of the dynamics is suitable to capture the key interactions between key players in the immune systems and the virus concentration. This project will develop a workflow for a systematic identification of the correct functional forms (currently chosen arbitrarily) as well as identification of critical thresholds resulting in severe sepsis. We will design approaches of machine learning and artificial intelligence, to match the model behaviour to the large dataset available, which includes time series readings of an extensive set of markers characterising the mice immune status at multiple time points. The ultimate goal is to be able to design early intervention strategies that can avoid the rapid escalation and the need for drastic action.
The typical academic requirement is a minimum of a 2:1 undergraduate degree in biological and health sciences; mathematics and computer science; physics and astronomy or a relevant discipline.
Candidates should be interested in AI and big data challenges, and in (at least) one of the three research themes. You should have an aptitude and ability in computational thinking and methods (as evidenced by a degree in physics and astronomy, medical science, computer science, or mathematics, for instance) including the ability to write software (or willingness to learn it).
This scholarship is open to UK and international candidates (including EU and EEA).
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This scholarship covers the full cost of tuition fees and an annual UKRI standard stipend (currently £15,285 for 2020/21).
Additional funding is available for training, research and conference expenses.
Please visit our website for more information.
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