Research Associate Deep Learning for CBRN Spread Prediction, UNSW Canberra
Employment type: Full time, 35hrs
Duration: Fixed term for a period of up to 12 months
Remuneration: Level A from $106,337 plus 17% Superannuation
Location: Canberra, Australian Capital Territory
About UNSW Canberra
University of New South Wales (UNSW) in Canberra has multiple locations in the Nation’s Capital.
UNSW Canberra distinguishes itself from other institutions by its commitment to being thoughtful, practical, and purposeful in all endeavors. This combined approach is integral to the university's impact and contributes to its recognition as one of the top 20 universities globally, as well as a proud member of Australia's esteemed Group of Eight. Choosing a career at UNSW means embracing an environment where thriving, facing challenges, and engaging in meaningful work are not just encouraged but integral to the university experience. If you seek a career where you can excel and contribute meaningfully, you've found the right place.
At UNSW, we pride ourselves on being a workplace where the best people come to do their best work.
Why Your Role Matters:
The School of Engineering and Technology (SET) offers a flexible, friendly working environment that is well-resourced and delivers research-informed education as part of its accredited, globally recognised engineering and computing degrees to its undergraduate students. The School offers programs in electrical, mechanical, aeronautical, and civil engineering as well as in aviation to graduates and professionals who will be Australia’s future technology decision makers.
Responsibilities:
Reporting to the Lead Investigator of the grant, the Research Associate position will involve research activity aimed at developing deep learning-based models for predicting the spread of CBRN threats and tasking teams of uncrewed aerial and ground vehicles to collect necessary data to improve predictions.
Who You Are:
Our ideal candidate will have experience in designing deep learning models to make predictions based on various timeseries data and images along with the following:
- A PhD in deep learning, computer vision, or a related discipline.
- A demonstrated ability to conduct innovative and independent research.
- A record of papers in high quality journals and/or conferences of high ranking in the field.
- Experience in programming in Python.
- Ability to conduct tutorials in use of deep learning models and libraries in a University environment and willingness to undertake teaching duties as required.
- Excellent interpersonal, oral and written communication skills appropriate for interacting effectively team members, collaborators and colleagues across the Faculty.
- Experience with real and simulated robotics, and sensor data will be highly regarded.
- Demonstrated ability to work as a member of a multi-disciplinary team showing initiative and taking direction as appropriate to the situation.
- Demonstrated ability to complete tasks within agreed time frames, with suitable supervision.
- Knowledge of health and safety responsibilities and the ability and capacity to implement required UNSW health and safety policies and procedures.
Benefits and Culture:
UNSW is committed to helping staff balance work-life responsibilities, by providing access to high-quality services, facilities, and flexible work and leave arrangements.
- Generous superannuation contributions
- Employee discounts
- A commitment to lifelong learning
- UNSW-wide strategy to focus on Healthy Body, Heathy Mind, Healthy Places and Healthy Culture.
Eligibility:
The successful candidate will be required to undertake pre-employment checks prior to commencement in this role. The checks that will be undertaken are listed in the Position Description. You will not be required to provide any further documentation or information regarding the checks until directly requested by UNSW.
The successful candidate will be required to work from the UNSW Canberra campus. To be successful you will hold Australian Citizenship. UNSW is unable to offer visa sponsorship for this position.
The University reserves the right not to proceed with any appointment.
How to apply:
Make each day matter with a meaningful career at UNSW. Please apply via our online recruitment system.
In your application, please include:
- your CV
- a 2-page pitch addressing the skills and experience outlined in the Position Description.
In order to view the Position Description – please ensure that you allow pop-ups for Jobs@UNSW Portal.
Contact: Matt Garratt E: m.garratt@unsw.edu.au
For questions regarding the application process - please email unswcanberra.recruitment@unsw.edu.au
For further information about UNSW Canberra, please visit: UNSW Canberra
For further information on living in Canberra, please visit: Living in Canberra
Find out more about the lifestyle and benefits when working with UNSW
UNSW is committed to evolving a culture that embraces equity and supports a diverse and inclusive community where everyone can participate fairly, in a safe and respectful environment. We welcome candidates from all backgrounds and encourage applications from people of diverse gender, sexual orientation, cultural and linguistic backgrounds, Aboriginal and Torres Strait Islander background, people with disability and those with caring and family responsibilities. UNSW provides workplace adjustments for people with disability, and access to flexible work options for eligible staff.
