Research Fellow in Wearable Technologies Ref: 5216
School/department: School of Engineering and Informatics - Department of Engineering and Design - Sensor Technology Research Centre
Hours: Full time. Requests for flexible working options will be considered (subject to business need).
Contract: Fixed term until 31 October 2020
Salary: Starting at £33,797 to £40,322 per annum, pro rata
Placed on: 18 December 2020
Closing date: 20 January 2021. Applications must be received by midnight of the closing date.
Expected interview date: First week in February 2021
Expected start date: 01 March 2021
The Sensor Technology Research Centre at the University of Sussex is looking for a full time research fellow with a strong mathematical and computer science background to work with Dr Niko Münzenrieder and Dr Daniel Roggen on the research-intensive project "Shape sensing textile for orthotics - SmartSensOtics".
This project aims to develop a textile sleeve with an integrated array of stretch and bend sensors. Once this sleeve wrapped around a body part, such as a leg or an arm, a computational model must infer the shape of the sleeve based on the readings from the stretch and bend sensors. In the domain of orthotics, this would offer a digital alternative to traditional plaster cast molds. More generally, this technology could be an alternative to current motion capture systems using video markers or inertial measurement units.
Your role will be to create an algorithm and associated software to reconstruct the sleeve shape based on the stretch and bend sensor readings. The sleeve itself is developed by other research fellows. An initial step will involve simulating the deformation of a sleeve around a shape to simulate the readings of virtual stretch and bend sensors located on it. Based on this, a reconstruction algorithm will be developed, for example based on minimising an energy function, or using machine learning. Afterwards, you will interact with the research fellows in charge of developing the hardware to verify and improve your algorithm based on the acquisition of real sensor data.
This project is well suited for publications. Publications could include: shape reconstruction characterisation based on simulated deformation and simulated sensor models including effect of noise and non-linearities; shape reconstruction based on real data; motion capture; etc.
Additional information on the project to date is available in the paper "Lugoda et al., ShapeSense3D: textile-sensing and reconstruction of body geometries, Ubicomp/ISWC Adjunct Proceedings, 2019."
This project is based at the Sensor Technology Research Centre at the University of Sussex.
Key Requirements. This post is well suited to a highly motivated individual with excellent software programming skills, creativity, and ability to address multiple aspects of the project simultaneously, as well as a willingness to operate in a dynamic research environment within an international team.
Candidates should have a MSc or PhD degree in computer science, computer engineering, electrical engineering, mathematic, physics, or equivalent, with as a key requirement strong programming expertise. They should be able to tackle the problems making up this project, such as: shape deformation simulation using software libraries (e.g. an existing code base is available in Matlab and Python using PyChrono), mathematical modelling applied to physical/mechanical or equivalent systems, mathematical formulation of optimisation problems and their resolution through gradient descent and other optimisation algorithms, graphical visualisation, sensitivity analysis, etc.
The candidate should be able to interact with hardware engineers to define with them an experimental protocol to obtain the sensor readings for particular sleeve deformations, in order to verify the reconstruction method.
Background. The Sensor Technology Research Centre at the University of Sussex works on the interface between electrical engineering, computer science, and physics to develop advanced and innovative sensor systems for applications in sports, healthcare, or wearable electronics.
Advantages and career development. This short term position is ideally suited for somebody who wants to broaden his/her knowledge in sensors, wearable technologies and mathematical modelling, to support patients and professionals in the healthcare sector.
Please contact Daniel Roggen (email@example.com) for informal enquiries.
The University is committed to equality and valuing diversity, and applications are particularly welcomed from women and black and minority ethnic candidates, who are under-represented in academic posts in Science, Technology, Engineering, Medicine and Mathematics (STEMM) at Sussex.
The University of Sussex values the diversity of its staff and students and we welcome applicants from all backgrounds.
How to apply
Download our academic application form - Dec 2020 [DOC 199.50KB] and Personal details and equal opportunties form [DOC 108.50KB] and fill in all sections.
Email your completed application, and personal details and equal opportunities form, to firstname.lastname@example.org
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You can also send your application by post to Human Resources Division, Sussex House, University of Sussex, Falmer, Brighton, BN1 9RH.