Research Associate in Machine Learning for Information Networks

Applications are invited for a Research Associate (RA) within the Department of Automatic Control and Systems Engineering with the University of Sheffield (http://www.sheffield.ac.uk/acse) 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.

You should have, or be close to completing, a PhD degree (or have equivalent experience) in signal processing, electrical engineering, aerospace engineering, mathematics, statistics, physics or a related area. You should also have knowledge and experience of Gaussian process methods, message passing and other Bayesian approaches for nonlinear systems and inference. Additionally you should have effective communication skills along with software skills.

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.

Making sense of multiple heterogeneous data is a challenging task that has been extensively studied, but the provision of reliable solutions for autonomous and semi-autonomous systems is a task that remains only partially solved. Fusion of data from multiple heterogeneous sensors of this type is part of the challenge; even more so when the autonomous decisions have to be performed in sequentially and in real-time. Capturing confidence and uncertainty from the integration of heterogeneous large-scale data remains a challenging task. This project will develop pioneering approaches that can be used in safe and reliable autonomy at different levels in sensor network systems. The main focus of SIGNetS
is on: 1) developing approaches for information networks and providing solutions with trust and hence quantifying the impact of uncertainties on the final solutions, 2) scalable methods for sensor data usion, inference and intentionality prediction and 3) sensor management of these scalable self-learning networks. SIGNetS will provide new methodology for the area of decentralised sensors in large scale surveillance, reconnaissance and intelligence gathering scenarios. The focus is on large numbers of sensors with many differing modalities which need to cooperate amongst each other without access to a fully centralised communications and processing architecture.

Uncertainty Quantification and Sensor Data Fusion
We will consider the fusion of large quantities of heterogeneous data in order to generate new and enhanced Situational Awareness and Autonomy. The ideal candidate will have knowledge of probabilistic models and methods. 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.

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