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Research Assistant/Associate in Machine Learning for Information Networks

University of Sheffield - Department of Automatic Control and Systems Engineering

Location: Sheffield
Salary: Either (Grade 6) £26,715 to £30,942 per annum or (Grade 7) £31,866 to £33,797 per annum
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 14th January 2021
Closes: 31st January 2021
Job Ref: UOS027126

Contract type: Fixed-term for 24 months.

Faculty: Faculty of Engineering

Applications are invited for a Research Assistant/Associate within the Department of Automatic Control and Systems Engineering with the University of Sheffield ( to work on the project: “SIGNetS – Signal and Information Gathering for Networked Surveillance”. SIGNetS is a collaborative project between the University of Sheffield, University of Cambridge and the University of Surrey.

To be considered for this post, you must have a good first degree (minimum class 2:1) and a PhD degree (or be close to completion/have equivalent experience) in signal processing, electrical engineering, aerospace engineering, mathematics, statistics, physics, or a related area. The following background will be useful: statistical signal processing, probability theory, Bayesian inference optimisation methods or equivalent experience, as well as experience of developing MATLAB software and familiarity with related toolboxes. You will also be experienced in conducting research projects both individually and as part of a team.

Flooded with information, information networks and decision making systems have to be able to cope with the deluge of data and hence solve efficiently complex and high dimensional problems. Conventional methods fall short in providing reliable solutions in such cases and a new way of thinking, new methods are needed. This project aims at developing scalable Bayesian approaches able to solve complex and high dimensional problems with multi-sensor data. One such problem is tracking groups and extended objects.

We will consider the fusion of large quantities of heterogeneous data in order to generate new and enhanced Situational Awareness and Autonomy. The project focus is on uncertainty quantification arising directly from computed posterior probabilities, which are obtained by scalable approximate procedures such as Gaussian Process methods, Message Passing and Variational Inference. The successful candidate is expected to provide mathematical results for the trustworthiness of the results of the developed approaches and algorithms. Another aspect of the project focuses on groups and extended object tracking and uncertainty quantification of the proposed solutions. These are linked with learning and intent prediction.

The project includes tight collaboration with the University of Cambridge and the University of Surrey, regular meetings and deliverables to our funders.

We’re one of the best not-for-profit organisations to work for in the UK. The University’s Total Reward Package includes a competitive salary, a generous Pension Scheme and annual leave entitlement, as well as access to a range of learning and development courses to support your personal and professional development.

We build teams of people from different heritages and lifestyles from across the world, whose talent and contributions complement each other to greatest effect. We believe diversity in all its forms delivers greater impact through research, teaching and student experience.

To find out what makes the University of Sheffield a remarkable place to work, watch this short film:, and follow @sheffielduni and @ShefUniJobs on Twitter for more information.

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