The FMRIB Analysis Group, OHBA (the Oxford MEG Centre) and the Donders Institute, Nijmegen, Netherlands have jointly received from the Wellcome Trust a Strategic Award to develop Integrated Brain Imaging for Neuroscience Research and Clinical Practice. We are looking for excellent researchers with a strong technical background, ideally in developing Neuroimaging Analysis Methods (approaches, algorithms and software) for MRI and MEG, but also with experience in other areas of Engineering/Applied Mathematics, Statistics, Computer Science and Physics. This work will feed into future versions of FSL.
Several positions are to be filled in the areas of functional and structural brain modelling for MRI and MEG data.
Formal adverts have now been placed for two positions at FMRIB, and positions at OHBA and Donders will be advertised shortly.
https://www.recruit.ox.ac.uk/pls/hrisliverecruit/erq_jobspec_version_4.jobspec?p_id=103554
https://www.recruit.ox.ac.uk/pls/hrisliverecruit/erq_jobspec_version_4.jobspec?p_id=103552
Project overview:
We have recently been awarded a 40-person-year Strategic Award by
the Wellcome Trust – "Integrated Multimodal Brain Imaging for Neuroscience Research and
Clinical Practice". Advances in neuroimaging have given unprecedented access to in vivo
measurements of brain function, structure and connectivity, but the full potential is not being
realized due to a lack of suitable analysis tools to explore relationships between, and
integrate across, modalities. Our overall goal is to bring multimodal imaging to the forefront
of neuroscience and clinical research in order to provide new biomarkers and insights into
disease mechanisms, explore aging and developmental processes, increase the scope for
large neuroimaging studies and improve clinical decision-support for patients. We will
generate new methodology, algorithms and software tools for integrated modelling and
analysis of multimodal neuroimaging data: specifically, structural MRI, diffusion MRI,
resting/task functional MRI and MEG. Key technical goals are: parcellation of the brain into
functional and structural units, and alignment of brain structure and function across multiple
subjects, in both cases utilizing all modalities simultaneously; computational modelling of structural
and functional networks in the brain that mediate information flow; developing machine learning
methods for multivariate classification and probabilistic parametric atlases using structural, functional
and network information, to develop biomarkers and investigate disease mechanisms. To maximize
practical impact we will be translating these methods into user-accessible, well-supported
software tools for both basic and clinical neuroimaging scientists worldwide.
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