UCL Queen Square Institute of Neurology
Wellcome Centre for Human Neuroimaging
(Senior) Research Fellow: Methods developer (x2)
We are looking for two post-doctoral researchers to join the Centre’s Methods group. They will work on methodological research to define, implement and deploy strategies for analysing high-resolution functional neuroimaging data from ultra-high magnetic field MRI (7T or more).
Since 2019, in-house facilities of the WCHN include a 7T Siemens Terra MRI scanner with parallel transmission capability, providing access to functional and anatomical information about the human brain in vivo with very high precision, spatial specificity and interpretability. The Centre has pioneered many neuroimaging methodologies, including Statistical Parametric Mapping (SPM) and Dynamic Causal Modelling (DCM). The Methods Group specialises in the development of a broad range of image processing methods based on principled generative models of image data and statistical analyses aimed at interpreting neural signals, see: Methods | Wellcome Centre for Human Neuroimaging | FIL | UCL
The appointees will engage in their own research projects with flexibility to shape their research directions, in line with the overall goals of the Centre. The developed methods will be made available to Centre researchers, as well as others in the international research community, through the widely used SPM software, which is developed in the Centre. Examples of possible projects include:
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Development and optimisation of analysis techniques exploiting 7T fMRI data to probe discrete units of neuronal computation such as layers, columns, stripes and small sub-nuclei;
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Development of generative models of anatomically-informed fMRI analyses, for layer-specific, surface-based investigation of cortical processing at 7T;
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Devising correction schemes to mitigate imaging artefacts caused by magnetic field inhomogeneities, motion and physiology;
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Development of a statistical framework for the multiple testing problem in the context of laminar-specific fMRI analyses.
The appointees will liaise closely with the Centre’s Methods, Physics and Computational Anatomy teams to provide solutions to challenges posed by ultra-high field imaging that integrate both acquisition and modelling strategies. Collaboration with neuroscientists from other disciplines working at the Centre is another important aspect of the role, including the provision of advice and guidance on best practice neuroimaging strategies.
The posts are available immediately and are funded by a grant from the Wellcome Trust until 30 November 2023 in the first instance.
Applicants must have a PhD (Senior Research Fellow) or, if not already held, the PhD must be obtained by the agreed start-date (Research Fellow applicants only) in a topic related to numerical computing and methods development. Strong programming expertise (e.g. MATLAB or C/C++) and experience with ultra-high field MRI are essential. Senior Research Fellow applicants must also have demonstrable experience in relevant areas of methodological development and of leading projects to completion.
Appointments will be on UCL Grade 7 (Research Fellow), the salary for which ranges from £36,028 to £43,533 per annum (including London Allowance) or UCL Grade 8 (Senior Research Fellow), the salary for which ranges from £44,737 to £52,764 per annum (including London Allowance). Appointment at Grade 7 is dependent upon having been awarded a PhD; if this is not the case, initial appointment will be at research assistant Grade 6B (salary £31,542 - £33,257 per annum) with payment at Grade 7 being backdated to the date of final submission of the PhD thesis.
For more details and to apply see: https://tinyurl.com/1ccxrc93 or search via UCL’s online recruitment – http://www.ucl.ac.uk/hr/jobs where you can download a job description and person specification using ref: 1874477. For queries regarding the application process, please contact Oksana Shapoval, HR Officer, UCL Queen Square Institute of Neurology (email: ion.hradmin@ucl.ac.uk). Informal enquiries to Dr Guillaume Flandin (email: g.flandin@ucl.ac.uk).
Closing Date: 23 Feb 2021