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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.
Early career researcher (staff candidate) in the field of quantitative magnetic resonance imaging (ESR6)
 
Application deadline: 1st February 2018
 
Marie Curie Early Stage Researcher (staff PhD candidate) in the field of quantitative magnetic resonance imaging (ESR6)

Applications are invited for a 3 year full-time Marie Curie Early Stage Researcher (staff PhD candidate) in the field of quantitative magnetic resonance imaging.  This unique opportunity will enable an exceptional candidate to work in an international research training network, for the European H2020 MSCA ETN project, BQ-MINDED.

As one of two related posts within this project at the University of Sheffield (ESR6), your specific research, titled “Parallel transmission/reception and simultaneous multi-slice (SMS) techniques for dMRI”, will aim to reduce the acquisition time for dMRI measurements using state-of-the-art hardware- and software-based methodologies. It will involve exploring the optimal integration of simultaneous multi-slice (SMS) into quantitative MRI (Q-MRI), such as relaxometry, dMRI, and derived microstructural models. This will include the accurate characterization of the noise distributions resulting in SMS for different multi-transmitter and multi-receiver configurations and parallel RF pulse transmission protocols, and its exploitation in the parameters fitting for obtaining dMRI and relaxometry images. Finally, an optimal Q-MRI SMS protocol will be implemented taking into account clinically acceptable acquisition times and the regulations on specific absorption rate.
Further details can be found here, and applications are via the BQ-MINDED project website (www.bqminded.eu) or via jobs.ac.uk here

Early career researcher (staff candidate) in the field of quantitative magnetic resonance imaging  (ESR9)
 
Application deadline: 1st February 2018
 
Marie Curie Early Stage Researcher (staff PhD candidate) in the field of quantitative magnetic resonance imaging (ESR9)

Applications are invited for a 3 year full-time Marie Curie Early Stage Researcher (staff PhD candidate) in the field of quantitative magnetic resonance imaging.  This unique opportunity will enable an exceptional candidate to work in an international research training network, for the European H2020 MSCA ETN project, BQ-MINDED.

As one of two related posts within this project at the University of Sheffield (ESR9), your specific research, titled “Characterisation of brain tissue structure and vasculature in the micro-scale using dMRI” will involve studying the impact of the microstructural and microvascular changes in dMRI acquisitions, and proposing state-of-the-art signal models for estimating their defining parameters. In a first phase, you will develop computational models dependent on key parameters defining brain tissue. These models will be used for simulating dMRI signals to perform in silico sensitivity analysis of standard dMRI sequences to changes in the microstructure and the microvascular network. In a second phase, you will develop biophysically-oriented models, generalizing previous works, for reducing the variance in the estimation of the aforementioned structural and vascular parameters. The estimation of the model parameters will be assessed by computer simulations as well as using physical phantoms
Further details can be found here, and applications are via the BQ-MINDED project website (www.bqminded.eu) or via jobs.ac.uk here

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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