Sensor Technology Research Centre

Open positions

PhD Scholarship: Bootstrapping open-ended activity awareness in wearable computing

A PhD position and associated scholarship is available in the Wearable Technologies Lab (Dr. Daniel Roggen, at the University of Sussex (UK). The position is in the field of Wearable/Mobile/Ubiquitous Computing and Activity/Context Recognition and it is linked to the EPSRC research project "Lifelearn".

Closing date: Thursday, March 15, 2018

Project objectives

The data of wearable devices - often motion sensors - can be analysed with machine learning to infer human activities. A good illustration of this are wearable fitness trackers. However, wearables can be used to recognise much more complex or subtle activities, for instance in industrial settings, for sports performance analytics and for home assistance. Sufficient data, however, must be available to train machine learning models. This limits current wearables to recognise contrived set of activities, which is inadequate for the next generation of smart assistants.

The aim of this project is to develop systems capable of recognizing an open-ended set of activities. In this project you will develop methods to reduce the effort associated with acquiring machine learning training data which are used to classify wearable sensor signals (e.g. motion, sound) to activities. This is achieved by exploiting the growing availability of multimedia datasets which can be reused and translated (e.g. the recent University of Sussex-Huawei Locomotion dataset, the Google AVA dataset, Youtube) with implicit (e.g. system identification), explicit (e.g. transfer learning) shared representations or recent deep learning techniques.

The selected candidate will join an interdisciplinary research centre with state of the art computing and electronics facilities and a wide range of technologies at hand: augmented reality glasses, smartwatches, novel sensor technologies, ad-hoc sensor nodes, etc. Some of the research areas you may come in contact during your PhD include signal processing, applied machine learning, data mining, big data, cloud services, crowd-sourcing, sensor technologies, mobile/embedded programming, human-computer interaction, etc.

Skills required

The ideal candidates will have a master’s degree in computer science, computer engineering, electrical engineering, mathematics, or equivalent. They have a very strong interest in research and outstanding technical skills. They have a passion to contribute to the development of next generation wearable systems, and a strong interest in research at the crossroads of signal processing and machine learning, embedded systems, sensor technologies and their applications. Applicants should be committed to pursue leading research and publish results in top venues. Additionally, we expect mastery of written and spoken English, self-motivation, an inquiring mind, ability to work independently and in an interdisciplinary environment.

Funding notes

The Scholarship includes a three year stipend at a standard rate (currently £14,553 per annum) and, in addition, fees at the UK/EU rate. Since the scholarship only covers fees at the UK/EU rate any overseas applicants are kindly requested to state in their application how they propose to cover the difference between UK/EU and overseas fees.

How to apply

First contact Dr. Daniel Roggen ( with your CV to discuss the position.
Afterwards,applications are handled online via the doctoral school:

Self-funded PhD positions and visiting postdoc positions available

The Wearable Technologies Laboratory (Dr. Daniel Roggen,, part of the Sensor Technology Research Centre at the University of Sussex (UK), is welcoming candidates who are self-funded for PhD or Postdoc positions in the field of Wearable/Mobile/Ubiquitous Computing and Activity/Context Recognition.

The candidate must demonstrate the right skillset and provide a research plan to support his/her application. .

Contact Dr. Daniel Roggen ( with your CV and research statement.

How to apply & Further information