Saab Australia specialises in the development of Command and Control systems, primarily for the Australian Navy’s Fleet. Our software acts as the ‘brain’ of the ship – it interconnects the sensors and weapons while giving Navy operators the ability to interpret the battlespace and effectively defend against threats. Our Modelling and Analysis team develops simulations to assess the performance of our systems in the most challenging scenarios and provides support to our software engineers in the design of complex algorithms – basically, we do the maths to help design the system and to show the Navy that it works.
I’m working on the prototype for our automated engagement planning algorithm. This algorithm uses artificial intelligence to defend the ship against hostile anti-ship missiles; significantly improving the ship’s chance of survival.
Earlier this year, I designed an extension to the algorithm that allows the ship to more efficiently use defensive missiles. I began by designing this extension purely mathematically (i.e. with a pen, paper, and back of the envelope calculations) to give myself a feel for what types of solutions may/may not work. I then incorporated several alternatives of the extension into our prototype and simulated their performance over several hundred scenarios in which a ship has to defend itself against different types/quantities of anti-ship missiles. This allowed me to select the best design from my set of candidates, and to have a quantitative estimate for how well it works. I got the opportunity to present these results to officers of the Australian Navy, and my work was well received.
I grew up in Adelaide – I went to high-school at Concordia College and studied at the University of Adelaide. All throughout my life I enjoyed maths and was an (almost obsessively) hard worker. I skipped a grade in maths at high school, completing year 12 maths in year 11 and then undertook Uni maths while in year 12. But I preferred the practical side of maths – seeing it as a tool to understand real-world phenomena and design practical solutions – and this drew me toward engineering.
I didn’t know what type of engineering I wanted to study initially, so I began in Adelaide Uni’s general engineering program (called ‘Flexible Entry’). After trying subjects from the various engineering disciplines, I developed an appreciation for the depth of electrical and electronic engineering (it’s not just circuits and power lines; it’s the theory that underpins all modern technology). So, I ended out transferring to electrical and electronic engineering after my first year; going on to major in signal processing with an honours project that delved into a bit of artificial intelligence – which is now my main interest area.
My first engineering job was a summer internship with the Defence Science and Technology Group after my second year at Uni. I enjoyed my time there and found defence projects fascinatingly complex (my research project was on the design of target detection algorithms for high-resolution radar systems). To both broaden and leverage my experience, I decided to move to the defence industry and the following summer (after my third year of Uni), I started at Saab as an intern in Modelling and Analysis. Saab kept me on as a casual while I finished my engineering degree, and I transitioned into a full-time role upon completion.
Of course. Saab’s Modelling and Analysis team is comprised of individuals with a diverse range of technical backgrounds, ranging from technical experts (with broad system-wide knowledge, or narrow but highly specialised knowledge) to high achieving and motivated engineering graduates/undergraduates who can bring new perspectives to challenging technical problems. The key characteristics/skills for my role are closely aligned with Saab’s core values:
The subject matter aside (designing algorithms to intercept missiles with missiles at supersonic speeds), I appreciate the opportunity to learn new skills and to feel like I am providing a tangible contribution to the solution of a complex problem. After working hard over the course of several months to understand the automated engagement planning algorithm, the most satisfying moments this year for me were: 1) getting to design my own extensions to the algorithm (and seeing them actually work in simulation); and then 2) getting to present the results of my work to the Navy (which makes me feel like my work and opinion are valued and that what I’m doing has an impact).
Rather than discuss limitations per se I’ll highlight some of the salient differences between university and my professional engineering environment. Firstly, professional engineering is significantly less structured; problems are rarely well defined (i.e. the person asking the question doesn’t necessarily know the answer they want or give you the information you need) and sometimes there isn’t even an answer. As an engineer, you need to know what questions to ask to get the information you need and have the judgement to know whether it is practically solvable.
Secondly, when you’ve got a highly technical problem that you’ve been working on for a while, there are few people that know it better than (or even to the same level as) you. This means you may have limited technical support when you’re stuck, and you need to be really careful when explaining technical detail to peers and project stakeholders – sadly, not everyone will have the same palette/enthusiasm for multiple-objective optimisation theory as you might.
My key pieces of advice are: