Research Fellow in Human Activity Tracking

About the role 

This project is part of an EPSRC funded project across six UK universities, IntentMAPS: Anticipating Intent facilitated by Multi-scale Adaptive Passive RF Sensing. Tracking people and discovering what they are doing is a challenging task. The Project will use AI to detect the behaviour of individuals and crowds and extend the state of the art by working at scale and more complex environments.  Working with the other partners, we will explore use of the passive collection of radio reflections from next generation 6G technologies to locate people. At Sussex, we will process the location information provided by our partners and use this to track multiple people within a crowd. In addition, this can be used to perform human activity recognition at individual and group level. 

We seek someone to work with Dr Phil Birch and partners to develop deep learning algorithms and train the networks. You will be part of the Centre for Advanced Telecommunications and Signal Processing at Sussex and be also working with teams at Cambridge, Imperial College, Loughborough, Brunel and Manchester Met.    

About you 

The successful candidate should hold a PhD in Computer Vision, Engineering, Computer Science, Mathematics, Physics or a related subject. They must be skilled in Python and PyTorch or an equivalent. They must also be skilled in data preparation and processing, as well as training and evaluating deep learning models. The successful candidate should be able to develop new models and prepare journal publications. Experience with passive radio sensing is not essential.  

About our School 

The School of Engineering and Informatics is a research intensive environment that consists of two departments: Informatics, and Engineering and Design. 

Why work here

Find out more about our reward and benefits package.

Find out about our equality, diversity and inclusion

Further Key Information  

Please contact Dr Phil Birch p.m.birch@sussex.ac.uk for informal enquiries. 

For full details and how to apply, click the 'Apply' button above.

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.  

Please note that this position may be subject to ATAS clearance if you require visa sponsorship.  

The University of Sussex values the diversity of its staff and students, and we welcome applicants from all backgrounds. 

The University requires that work undertaken for the University is performed in the UK.  

Visa Sponsorship Queries: This role has been assigned an eligible SOC code, and meets the salary requirements for Skilled Worker Sponsorship if full time. Please consult our Skilled Worker Visa information page for further information about Visa Sponsorship

Advert information