Postdoctoral Researcher in Machine Learning and Wearables
School/department: School of Engineering and Informatics - Department of Engineering and Design - Sensor Technology Research Centre
Hours: full time
Contract: Fixed term for 12 months
Salary: starting at £32,004 and rising to £38,183 per annum
Closing date for applications: 19 June 2017 Applications must be received by midnight of the closing date.
Expected start date: ASAP, to be discussed.
The Wearable Technologies Lab at the University of Sussex is looking for a postdoctoral researcher to work with Dr Daniel Roggen on the research-intensive project "Mobile Activity Recognition with Machine Learning and Deep Learning".
The Wearable Technologies Lab is in the process of collecting the world's largest dataset of modes of transportation of mobile phones users in the wild, with accurate annotations.
This highly multimodal dataset may surpass 1TB once completed in the summer. It sets a new standard for the community to benchmark activity recognition methods and will be released publicly. Reference publications using this dataset have a strong potential for high citations.
Your role will be to analyse the dataset and to develop reference activity recognition methods based on classical machine learning to recognise modes of transport and user activities.
In a second phase you will investigate novel approaches based on deep learning to exploit the temporal dynamics of the dataset and learn suitable representations from the data which may further enhance performance.
This project is based at the growing Wearable Computing Group at the University of Sussex This project gives the opportunity to collaborate with a large multinational interested in behaviour analytics which is associated with this project. It also gives possibility to patent some of the research outcomes.
Key Requirements. This post is well suited to a highly motivated individual with excellent technical skills and with a willingness to operate in a dynamic research environment within an international team.
Candidates should have a PhD (or will shortly be assessed for a PhD) in Computer Science, Mathematics or Electrical Engineering with a strong background in (one or more of) Machine Learning, Time Series Analysis, Signal Processing, Data Mining and ideally Deep Learning.
An established expertise in Activity Recognition and Wearable/Mobile Computing is desired. The candidate should have a strong interest in the combination of theoretical and experimental research.
Background. The Wearable Technologies Lab of Dr. Daniel Roggen at the University of Sussex develops novel wearable sensors and methods to recognize and understand human activities with applications to sports, healthcare, entertainment and industry.
Advantages and career development. This position is ideally suited for somebody who wants to broaden his/her knowledge in wearable or ubiquitous computing and activity recognition and that is interested in deep learning research.
The project is suitable for high impact publications as they will lead to reference performance benchmarks on the world's largest dataset of modes of transportation and will allow the candidate to gain international visibility. The project gives a chance to collaborate with a major industrial player interested in behavioural analytics.
The candidate will be supported in applying for grants to support his/her further career development.
For any further information and informal inquiry contact Dr Daniel Roggen: firstname.lastname@example.org
More details about the nature of the work can be inferred from past publications.
More information about the Wearable Technologies Lab.
How to apply
Applications should be accompanied by a full CV and a statement of how you envisage your role..
Email your completed application and personal details and equal opportunities form to email@example.com
You should attach your application form and all documents to the email (don't use a web-based upload/weblink service) and use the format job reference number / job title / your name in the subject line.
You can also send your application by post to Human Resources Division, Sussex House, University of Sussex, Falmer, Brighton, BN1 9RH.