Lipids (fats) are molecules that are essential for life: (i) for energy metabolism and storage, (ii) for signalling during inflammation and development and (iii) they comprise the membranes that hold our cells together. Large lipidomics mass spectrometry datasets are available that have the potential to provide biological insight, in order to understand underlying disease processes and identify new biomarkers. However, researching lipids requires novel computational approaches to enable efficient processing and analysis of these datasets.
This project will look at the problem of developing robust, scalable and efficient processes to identify significant similarities and anomalies within lipidomics datasets. Initial focus will be on a motif/graphlet based approach, which has been demonstrated to be successful in other domains, including gene analysis (Machanick, P. and Bailey, T.L., 2011. MEME-ChIP: motif analysis of large DNA datasets. Bioinformatics, 27(12), pp.1696-1697.) and social networks (Topirceanu, A., Duma, A. and Udrescu, M., 2016. Uncovering the fingerprint of online social networks using a network motif based approach. Computer Communications, 73, pp.167-175.). However, motif approaches are computationally expensive, and this project will require novelty to allow comparison based on larger motifs.
The PhD is suitable for someone keen to develop their knowledge of state-of-the-art AI and data science techniques and apply these for important biological applications with the potential for significant impact. The student will benefit from a multidisciplinary supervisory team whose previous collaborations led to the development of LipidFinder, a bespoke tool to support computational pipelines for lipid analysis, enabling publications in Bioinformatics and Cell Metabolism.
The UKRI 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.
This project is a collaboration between Cardiff University School of Computer Science and Informatics in cooperation with the Cardiff University Systems Immunity Research Institute. The research themes are: T2: biological, health and clinical sciences (medical imaging, electronic health records, bioinformatics) and T3 - novel mathematical, physical and computer science approaches
Eligibility
The scholarships are open to UK/home and international candidates. For more information on eligibility, please visit the UKRI CDT in Artificial Intelligence, Machine Learning & Advanced Computing website
Entry Requirements
A 2:1 Honours undergraduate degree or a master's degree, in computing or a related subject. Candidates should be interested in AI and big data challenges, and in the research theme - novel mathematical, physical and computer science approaches.
Applicants whose first language is not English must demonstrate their proficiency by obtaining an IELTS score of at least 6.5 overall, with a minimum of 6.0 in each skills component. (https://www.cardiff.ac.uk/study/international/english-language-requirements)
Applications
Please visit the CDT website http://cdt-aimlac.org/ and follow the instructions.
Applicants should apply for postgraduate study via the Cardiff University webpage: https://www.cardiff.ac.uk/study/postgraduate/research/programmes/programme/computer-science-and-informatics,
Select the programme Doctor of Philosophy in Computer Science & Informatics with a start date of 1st October 2021, and upload these documents with your application:
• your CV
• a personal statement/covering letter
• two reference letters
• current academic transcripts
In the research proposal section of your application, please specify the project title and supervisors of this project. If you are applying for more than one project, please list the individual titles of the projects in the text box provided. In the funding section, please select ’I will be applying for a scholarship/grant’.
To complete your application please email a pdf(s) of your application to [Email Address Removed]
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