Fully funded PhD Scholarship – Machine learning & AI for activity recognition (2019)
A 3-year fully-funded PhD position is available in the Wearable Technologies Lab at the University of Sussex.
Type of award
Postgraduate Research
PhD project
== Objectives ==
AI and machine learning techniques can be used to infer human activities by interpreting the data originating from a variety of multimodal wearable, mobile and ambient sensors.
This can be used to provide contextual assistance with applications in human-robot interaction, healthcare, industrial assistance, sports training, skill assessment, entertainment, and others.
Within this project, you will seek methods to make it easier to train systems to recognise a wider range of human activities. You will research advanced machine learning and AI techniques to recognise a growing set of activities from multimodal sensors, and reduce the effort associated with acquiring annotated training data.
Depending on your interests, different approaches can be followed: deep transfer learning to exploit the growing availability of multimedia datasets (e.g. Google AVA dataset, Youtube data, the Sussex-Huawei dataset), interactive machine learning, crowd-sourcing, adaptive machine learning, and others.
The project can be oriented towards methods achieving high performance for offline usage, or methods suitable for real-time activity recognition running on embedded platorms. Demonstrators arising from this project are welcome.
== About the Lab ==
The Wearable Technologies Lab, led by Dr. Daniel Roggen, has been established in 2014. Since then, it has acquired funding from Google, Huawei, EPSRC, Unilever, the Austrian FFG, and others.
The focus of our lab is to advance AI techniques to automatically recognise and understand human activities or daily routines from wearable and mobile sensors. We have developed several wearable sensing platforms and software frameworks for this, including deep learning and ASIC-friendly approaches.
The lab has created numerous dataset for activity recognition research, the most recent is a massive transportation dataset – the Sussex-Huawei Locomotion dataset (www.shl-dataset.org) – which has been used in two prominent machine learning challenges at Ubicomp 2018 and Ubicomp 2019.
Some of our applications are in the fields of sports performance, industrial assistance, mobility monitoring, crowd behaviour analytics and healthcare.
The members of the lab have an international outlook, with a mix of computer scientists, computer engineers, and electronic engineers.
The lab has state of the art computing and electronics facilities with a wide range of technologies at hand: GPU computing platforms, augmented reality glasses, smartwatches, a vast array of datasets and ad-hoc software tools to support research, numerous novel sensor technologies and sensing platforms, etc.
Amount
£15,009 stipend, plus fees (at the UK/EU rate).
You will receive a tax free stipend at a standard rate of £15,009 per year for three years. In addition, your fees will be waived for three years (at the UK/EU rate).
Overseas applicants are welcome to apply if they can meet the fee shortfall.
Deadline
16 August 2019 17:00 (GMT)
How to apply
Contact Dr. Daniel Roggen (daniel.roggen@ieee.org) with your CV to discuss your application.
More informations about the Wearable Technologies Lab and ongoing research:
Apply online for a full time PhD in Informatics (SEP2019) using our step by step guide (http://www.sussex.ac.uk/study/phd/apply). Here you will also find details of our entry requirements.
Please clearly state on your application form that you are applying for the ‘Machine learning & AI for activity recognition’.
Contact us
phd@informatics.sussex.ac.uk
Official website