Optimisation for Personal Preferences
Engineering, IT, Mathematics and Statistics
- Due to the sensitivity and security of this project, students must have Australian Citizenship to apply. Any applicants not meeting this requirement will automatically be deemed ineligible for this project.
- The Industry Partner has implemented appropriate preparations to comply with Federal and State Government requirements regarding COVID-19 safety. Due to remote arrangements, this internship is now accepting applications from eligible PhD students nationwide.
ABOUT THE INDUSTRY PARTNER
The Science, Technology, Engineering Leadership, and Research Laboratory (STELaRLab) is Lockheed Martin Australia’s national research and development operations centre, and Lockheed Martin’s first non-US based, multidisciplinary R&D laboratory. It is headquartered in Melbourne, with research teams also located in Adelaide and Brisbane. STELaRLab undertakes R&D into information-related technologies: artificial intelligence, machine learning, analytics, image and signal processing, tracking & sensor fusion, and complex optimisation. Lckheed Martin apply those skills to a diverse range of areas including space situational awareness, space operations, multi-domain operational analysis, all-source information fusion, automated information extraction and interpretation, and hypersonics.
WHAT’S IN IT FOR YOU?
An internship with the STELaRLab will enable you to:
- Gain relevant professional experience working with a distributed and multidisciplinary corporate R&D team
- Work on a significant problem with a direct, meaningful impact
- Perform research in an emerging field spanning computer science, artificial intelligence, and user-centric design
RESEARCH TO BE CONDUCTED
STELaRLab has ongoing research programs relating to how optimisers for combinatorial problems can be used as aids to help reduce the cognitive burden on the user. These problems are such that the space of feasible solutions that need to be considered grows exponentially as the scenario increases in complexity. When used in such a context, it is important that the user feels comfortable that the tool would produce outputs that satisfactorily replicate the kinds of decisions which they would make themselves.
Such optimisers typically minimise some measure of the cost of a given solution. For example, in route planning, this could be the distance covered, the fuel consumption, tolls incurred, travel time, or some combination thereof. Within the set of tuneable parameters that control the cost, the hypothesis is that there exists one setting that that most closely mirrors the user’s own preferences. The question remains, however, as to how this setting can be determined, and whether their value should remain constant as time passes.
STELaRLab is seeking a PhD intern to help drive the design and implementation of a system where a user’s preferences can be captured within a software decision aid. This work, whilst focussed primarily on the computational aspects of the problem, seeks in the longer term to bridge between these computer science aspects towards a human-centric focus.
The intern will work, under the supervision of STELaRLab staff, to design a system capable of capturing input and formulating a preference model for a given user. This will involve the formulation and implementation of an optimisation system and a user interface, and there is potential scope to devise user surveys to measure the utility of such a system.
SKILLS WISH LIST
- Computer science, mathematics
- Experience using modern, high-level languages such as Python
- Adaptability and enthusiasm
- Human factors, psychology, trait theory
- Human-centred computing: human factors, explainable AI, human-machine interfaces
- Experience with off-the-shelf tools for combinatorial optimisation (such as for the travelling salesman problem, postal routing, and so on)
The aim of this project is to develop a proof-of-concept demonstrator which allows:
- A user to be surveyed regarding their preferences for solutions to a set of example problems;
- Personalised cost weightings to be developed for the user based on the gathered information; and,
- Further studies to be conducted regarding the usefulness of this information, with respect to the user’s preferences, as time elapses after the initial survey.
The intern will receive $3,000 per month of the internship, usually in the form of stipend payments.
It is expected that the intern will primarily undertake this research project during regular business hours and maintain contact with their academic mentor throughout the internship either through face-to-face or phone meetings as appropriate.
The intern and their academic mentor will have the opportunity to negotiate the project’s scope, milestones and timeline during the project planning stage.
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