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The development of novel statistical methods to analyse complex networks and multidimensional networks
Properties of network data have been explored in depth; however, there is lack of methodology for the analysis of many specific network data-types. A particular type of network data are multidimensional networks, which correspond either to relations evolving over time or to different relation types recorded between subjects. The research goal is to work with complex networks and multidimensional networks, developing novel methodology tailored for the analysis of such data, with particular focus on modelling the dependence between multiple networks using a latent variable construct and Bayesian inference. The purpose is to provide information on the interdependence between different types of relations among a group of subjects.
For more information, please refer to the job ad: https://www.tcd.ie/media/tcd/scss/pdfs/PHD_ad_Network.pdf.
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The development of novel statistical modelling approaches for dietary patterns analysis and their association with health outcomes (T-DIET)
The T-DIET project will develop novel statistical methodology to enable inference on dietary patterns, i.e., groups capturing different diets in a population, from longitudinal food intake data. The framework will rely on a Hidden Markov model (HMM), a type of latent variable model allowing to infer unobserved groups underlying longitudinal data, the dietary patterns. Further, it will allow one to model, in probabilistic terms, individuals' adherences to such patterns, permitting changes of diets over time, and directly quantifying uncertainty. Various complexities will be addressed, such as the compositional nature of intake data, or the incorporation of prior information available in the Nutrition literature on dietary patterns, e.g. their qualitative ordering. The methodology will be extended to incorporate the HMM into a quantile regression framework, to investigate the relationship between dietary patterns, individuals' adherence to such patterns, and health outcomes. The quantile regression framework will analyse relationships at all levels of health outcomes, and not only on average, as in standard regression approaches. The statistical methodology developed in the T-DIET project will have widespread potential application in the field of nutritional epidemiology. Last, open-source software implementing the methodology will be made available, to allow for its wide and feasible usage.
For more information, please refer to the job ad:
https://www.tcd.ie/media/tcd/scss/pdfs/PHD_ad___T_DIET.pdf.
Applicants should have (or expect to attain prior to project start) at least a 2.1 honours degree or equivalent in the areas of mathematics, applied mathematics or statistics. Applicants must demonstrate proficiency in statistical modelling and have some experience with statistical computing through R, python or C. Applicants for whom English is a second language will be required to demonstrate their competence in the English language in line with Trinity College Dublin requirements as appropriate.
Applicants should email Dr. Silvia D'Angelo (dangelos@tcd.ie) to apply. The application should include a 2-page comprehensive CV, academic transcripts
of the degree/ degrees, and a short cover letter/statement of purpose (2-pages max) indicating how their skills align with the project and their motivation for applying. Please include "PhD Application (T-DIET)" or "PhD Application (Statistical Network Analysis)" followed by your name in the subject line. The application CV should, at minimum, include the applicant's name, educational institution, qualification stating overall grade/percentage (predicted grades
are acceptable for those still studying) and contact details of two academic referees. Informal queries can be made to: dangelos@tcd.ie. Please include "PhD Query (T-DIET)" or "PhD Query (Statistical Network Analysis)" followed by your name in the subject line.
For more details on the hosting Institution and the posts, please visit the vacancies page of the School of Computer Science and Statistics, Trinity College Dublin: https://www.tcd.ie/scss/vacancies/ .
Best,
Silvia D'Angelo
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Silvia D'Angelo, PhD (she/her)
Assistant Professor in Statistics
School of Computer Science and Statistics
Trinity College Dublin
Ireland
Email: dangelos@tcd.ie
Website: https://sites.google.com/view/silviadangelo/home
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