PhD Studentship - Learning the Associations Between Brain Connections and Function/dysfunction
University of Nottingham - Mathematical Sciences
|Funding for:||UK Students, EU Students|
|Funding amount:||£14,553 stipend per annum|
|Placed on:||20th March 2017|
|Closes:||30th March 2017|
Location: University Park
Modelling and Analytics for Medicine and Life sciences Doctoral Training Centre: PhD Scholarship - Learning the associations between brain connections and function/dysfunction
Supervisors: Dr Theo Kypraios (School of Mathematical Sciences), Professor Stam Sotiropoulos (School of Medicine)
Understanding the workings of the human brain is one of the most outstanding challenges of our time. In particular, determining factors that contribute to the individual signature of integrated cognitive function is of genuine interest to neuroscience, but also of paramount importance for neurological applications; characterizing the "normal" brain structure and function is key for characterizing abnormalities and approaching disease mechanisms. Non-invasive and in-vivo magnetic resonance imaging (MRI), as well as Magneto-encephalography (MEG), can uniquely shed light to these questions.
This project will capitalise on advances and data offered by the cornerstone Human Connectome Project (HCP) (www.humanconnectome.org), for which the principal supervisor (SS) has been a major contributor. We will build novel computational methodology for estimating connections using complementary MRI and MEG through state-of-the-art inference techniques. In particular, we will develop models for estimating network structure from multimodal data and we will explore causal interactions. Using data-driven exploratory analysis, we will then identify latent associations between brain organisation and function. This will further allow the extraction of summary imaging-derived measures with certain contextual relevance that could comprise potential markers for subsequently exploring pathology-induced abnormalities and dysfunction. For instance, we will identify predictive behavioral traits of psychiatric disorders and explore their associations with estimated connectivity.
The MAML programme:
The MAML doctoral training programme focuses on innovative modelling, simulation and data analysis to study real-world problems in medicine and biology. Maintaining a healthy society creates major challenges in areas including ageing, cancer, drug resistance, chronic disease and mental health. Addressing such challenges necessitates continuing development and implementation of a raft of new mathematical approaches and their integration with experimental and clinical science. Students will apply mathematical approaches (from areas such as dynamic modelling, informatics, network theory, scientific computation and uncertainty quantification) to research projects at the forefront of biomedical and life sciences identified through well-established collaborations with both academic and industrial partners.
MAML students will be provided with an excellent training environment within the Centre for Mathematical Medicine and Biology and collaborating departments. Students will undertake tailored training, complemented by broadening, soft-skills, wet-lab (where appropriate) and student-led activities. There will also be opportunities for training and exchanges with world-leading partners.
These 3.5 year PhD scholarships start in September 2017. Successful applicants will receive a stipend (£14,553 per annum for 2017/8) for up to 3.5 years, tuition fees and a Research Training Support Grant. Fully funded studentships are available for UK applicants. EU applicants who are able to confirm that they have been resident in the UK for a minimum of 3 years prior to the start date of the programme may be eligible for a full award, and may apply for a fees-only award otherwise
Please apply via the Training Centre website: http://www.nottingham.ac.uk/mathematics/maml Applicants for the MAML programme should have at least a 2:1 degree in mathematics, statistics or a similarly quantitative discipline (such as physics, engineering, or computer science).
Completed applications and references should be submitted by Midnight GMT Thursday, 30 March 2017.
For any enquiries please email: PMfirstname.lastname@example.org
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