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Research Fellow in Modelling of Fixational Eye Movements

Are you an ambitious researcher looking for your next challenge? Would you like to apply your expertise in theoretical physics or mathematics to a biological problem? Would you like to work with exciting multidisciplinary teams including vision scientists, experimental psychologists, instrumentation engineers, and computational modellers to understand how we process visual information?    

We are seeking to appoint a Research Fellow in biological applications of theoretical physics and mathematics, funded under the UKRI ‘Building Collaboration in the Physics of Life’ scheme. The project, “A fresh look at visual sampling: (PhysFEM) How are fixational eye-movements optimised?” is in collaboration with Oxford University Department of Experimental Psychology and US collaborators. The project brings theoretical and numerical modelling (Leeds) together with advanced adaptive-optics retinal imaging, positional measurement and stimulation, and computation (Oxford) to explore the hypothesis that fixational eye-movements (FEMs) are optimised to tasks.

You will work with Prof. Daniel Read to develop analytical and numerical models of the trajectories of fixational eye movements (FEMs), exploring their influence on biological visual processing, and investigating the characteristics of FEM paths that are most optimal and useful to the observer under a range of assumptions and constraints. Working closely with the experimental team of Prof. Hannah Smithson at the University of Oxford, who measure fine-scale eye movements with an advanced Adaptive Optics Scanning Laser Ophthalmoscope (AO-SLO), you will be part of a core collaborative team, evaluating predictions from models against experiments. You will also work closely with a computational Research Fellow at Oxford and with Prof. David Brainard’s group at the University of Pennsylvania to transfer your new modelling capacity into the public domain via the ISETBio human vision modelling tool. 

You will have a PhD in Physics, Mathematics, or a closely allied discipline, with a strong background in theoretical (analytical and/or numerical) modelling of physical or biological systems, and with expertise in at least one of statistical physics, information theory or Bayesian statistics. Ideally you would also have knowledge of one or more of: relevant biology, mathematical biology, biological physics, soft matter physics, vision science or image processing.

You will have the ability to conduct independent research and a developing track record of publications in international journals. In addition, you will have excellent communication, planning and team working skills with an ability to work in an interdisciplinary project. 


What we offer in return

  • 26 days holiday plus approx.16 Bank Holidays/days that the University is closed by custom (including Christmas) – That’s 42 days a year!
  • Generous pension scheme plus life assurance – the University contributes 21.6% of salary.
  • Health and Wellbeing: Discounted staff membership options at The Edge, our state-of-the-art Campus gym, with a pool, sauna, climbing wall, cycle circuit, and sports halls.
  • Personal Development: Access to courses run by our Organisational Development & Professional Learning team, and self-development courses including languages, Creative Writing, Wellbeing Therapies and much more.
  • Access to on-site childcare, shopping discounts and travel schemes are also available.

And much more!  


To explore the post further or for any queries you may have, please contact: 

Professor Daniel Read, Professor of Soft Matter

Tel: +44 (0)113 343 5124

Email: D.J.Read@leeds.ac.uk 

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Job Details
Location
Leeds - Main Campus
Faculty/Service
Faculty of Engineering & Physical Sciences
School/Institute
School of Mathematics
Category
Research
Grade
Grade 7
Salary
£37,099 to £44,263 p.a.
Due to funding restrictions, an appointment will not be made higher than £39,347 p.a.
Working Time
37.5 hours per week
Post Type
Full Time
Contract Type
Fixed Term (2 years - grant funding, to end by 31 March 2026, starting no later than 01 April 2024)
Release Date
Wednesday 25 October 2023
Closing Date
Sunday 19 November 2023
Reference
EPSMA1094
Downloads
Candidate Brief (PDF)
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