QuantIC Studentship co-funded by Thales, in Computing Science

QuantIC are excited to announce the PhD studentship, between the University of Glasgow, Quantic and Thales Scotland is now open – exploring the topic of Human-Machine teaming in situations with time-sensitive decision making, cognitive and data overload and significant information uncertainty. The project will explore the use of machine learning and other AI techniques to help fuse multi-source information and provide decision support for a team of humans.

A key challenge in data science and AI is how best to bring human insight, sophisticated analysis algorithms and large amounts of time-critical data. Many environments such as emergency or military services present a highly complex environment in which crews need to make potentially critical decisions relating to platform command and control, threat evaluation and interactions with other elements of the service. To make those decisions effectively they must be able to assimilate large quantities of information regarding their surroundings.
Thales is involved in systems design for vehicles with rapidly increasing levels of sensor and information systems, designed to give crew improved situation awareness. Such systems require Sensor Fusion to combine data from multiple sources. They apply Information Management and Information Exploitation (IM/IX) to filter raw data into information that is useful to the crew and require Information Presentation to provide the information such that vehicle crews can make correct and timely decisions.

Research structure

The research will involve analysis of the team roles, the design of example artificial agents which will act as virtual team members, will track team member attention and behaviour to monitor whether they are currently performing appropriately, or whether data or cognitive overload is kicking in, and decide whether they need extra support. This could include:

a) Dynamically changing degrees of freedom in sensing and control for different participants depending on their load, reducing the data flow to a level that is achievable in the current context.

b) Dynamic approaches to transitioning between human and machine control in a team setting, such that algorithmic semi-autonomous agents take over certain tasks, to permit the human team members to focus on critical aspects.

c) Use of Machine learning tools to analyse variability and uncertainty in information flows, with the goal of using this information to prioritise the most important and reliable information for decision makers.

The work will be based around a number of mini-projects, together with colleagues at Thales, where adaptations of specific challenges associated with their ongoing projects are linked in to the ongoing fundamental research in the Ph.D. The student will be primarily based at the University, but is expected to spend time at the Thales offices in Glasgow appropriate to the project work scope, and which could include data gathering and experimentation. This will allow novel ideas to be tested in a context that is relevant to the company, and where the staff at Thales have strong benchmarks to compare the performance with. Students will gain experience of both academic research and industry.

For more information, and how to apply, click here