Research Associate in Advance Care Research Centre

We welcome applications to fill a postdoctoral position on statistical data processing in assisted living technologies from candidates with scientific computing, applied mathematics or computational statistics background.

The post entails primarily independent research work but also some collaboration with other researchers from the New Technologies of Care Centre, a joint venture of the Schools of Engineering, Informatics, and Medicine. You will contribute to the research, analysis and development of randomised sketching algorithms for streaming sensor data in the context of online model learning.

The goal of the project is to identify structure in streaming data from various sensors and capture it in a “data sketch”, a low-dimensional structure that preserves the main features of the original data with respect to a given computational task. This utility is necessary to enable online model learning from data captured over prolonged periods of times e.g. months and years as in the setting of assisted living.

In this paradigm, model learning and decision making have to be done at the edge, according to privacy rules, and it is thus imperative that these computations are kept tractable whilst streaming data accumulate to prohibitively-large dimensions. The expected output is to deliver pioneering research on data-sketching strategies based on randomised numerical linear algebra to serve a number of processing tasks, including online model learning, noise filtering, and anomaly detection.

The post holder is expected to work closely with the PI to design the sketching algorithms subject to different specifications (memory, speed and complexity constraints), and analyse their performance theoretically and computationally. This will involve physiological data, motion monitors and video signals arriving in asynchronous fashion. We will explicitly consider vector and tensor data types.

A full list of main responsibilities and the knowledge, skills and experience required for the role can be found in the full job description.

UoE Vacancy Ref: 

967

Closing Date: 

Monday, May 31, 2021

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Contact Telephone: 

+44(0)131 650 2769