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CISTIB seeks proactive and talented junior and senior researchers with proven track record of publications in leading international journals and conferences. Candidates for PhD positions must hold a PhD degree and have expertise in the area of interest. Background or strong interest in biomedical engineering and proficiency in spoken and written English is essential.

We are interested in individuals with excellent communication and leadership skills, with the ability to work in a multidisciplinary and international team, contributing to the visibility of the centre in the international scientific community. The ability to interact with other disciplines is essential. The ideal candidate will cooperate with members of CISTIB working on related topics as well as with our collaborators at several academic institutions in the UK and Europe.
PhD Scholarship in Machine Learning for Population Imaging
Application deadline: September 2017
Funding for International/EU/UK Student
The PhD research project will focus on the intersection of medical image computing and machine learning to develop computationally efficient frameworks for analysis of big imaging data. The applications include learning shape and appearance models from populations and extending them to deep and/or non-parametric Bayesian models. The project will be flexibly tailored into more specific objectives based on the interest of the candidate and the required task in hand.

With the emergence of population imaging data bases (e.g. UK Biobank), conventional methods assuming linearity and homogeneity across the population are challenged by the heterogeneity of the imaging data. Generative models, such as probabilistic mixtures of PCAs, cluster the population into groups having more coherent morphologies, and model the population more naturally. However, extending these frameworks into large imaging data setting (n>1000) is a computationally challenging task. The applicant will focus on developing scalable generative/discriminative Bayesian models using imaging phenotypes that are extracted from the raw data.

The Scholarship/Funding 
The position is fully funded covering tuition fees and standard living costs.
How to apply
For more information or to apply, please see the details of the offer, or email (quoting the Job Reference mentioned) a detailed CV with publication list and a concise description of research interests and future plans to:
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