Research Fellow

Multimodal Machine Learning (ML) and
Physics-informed Machine Learning are
becoming increasingly utilised in many critical
agriculture. Multimodal ML aims to co-learn
data coming from multiple sources which may
differ significantly in data representation.
Multimodal ML not only learns to effectively
combine complementary information from
different modalities, but also deals efficiently
with practical challenges such as missing or
noisy data. For example, multimodal semantic
segmentation can process data from 2
- dimensions, e.g., hyperspectral, to 3
- dimensions, e.g., LiDAR, and data from
satellite sensors, UAV sensors or ground
sensors. Physics-informed ML integrates
physical laws and mathematical models into
learning systems to model and forecast the
dynamics of multiphysics and multiscale
systems.

We are seeking a Research Fellow to conduct
research into advanced techniques for
Multimodal ML and Physics-informed
Generative Adversarial Networks (GANs).

The Research Fellow will contribute to the
research agenda of the Faculty of Engineering
through undertaking research connected with
and developing from Advance Queensland
project funding: Artificial Intelligence Enabled
Mine Site Rehabilitation; and related funding.
They will research, develop and demonstrate
multimodal ML and temporal prediction in

This position is based at QUT Gardens Point
campus and will involve some field work to
demonstrate the outcomes within Queensland
and Australia.

This position reports to Prof Clinton Fookes for
supervision, workload management and for
Performance Planning and Review (PPR).

**Key responsibilities include**:

- Plan and carry out cutting edge research

that advances and demonstrates the goals
of the research project.
- Prepare and publish research outcomes in

relevant refereed journals, and present
research outcomes at conferences,
workshops and collaborator meetings.
- Participate in, and on occasions, lead the

creation of technology demonstrators for
assessing and validating the research
outcomes with the collaborators. Note this
may include field trips.
- Provide written and verbal reports as

required by projects and undertake
associated administrative functions.
- Develop positive working relationships with

other science and service staff at both QUT
and collaborating partners.
- Assist with supervision of PhD student

projects.
- Implementing and administering University

policy within the Faculty with respect to
equitable access to education and
workplace health and safety.
Research Fellow, Level A or Research Fellow
Level B depending on their level of attainment
against the position classification standards
outlined in the QUT Enterprise Agreement
(Academic Staff). The position classification
standards provide the basis to differentiate
between the various levels of appointment and
define the broad relationships between
classifications. The University reserves the
right to appoint at either classification level.

**Type of appointment**
This appointment will be offered on a fixed
- term, full-time basis for three (3) years.

**Location**

Gardens Point campus.

Selection Criteria
1. Completion of a PhD in a relevant

discipline preferably in Artificial
Intelligence, Computer Vision or Machine
Learning.

2. Experience and research track record in
Multimodal Machine Learning, Generative
Adversarial Networks and/or Physics
- informed Machine Learning.

3. Demonstrated ability to undertake high
- quality research via publication in top tier
conferences/journals in AI, CV, ML.

4. Demonstrated written communication
skills, including the ability to maintain
accurate records, and experience in
summarising scientific data in reports.

5. Demonstrated experience in all aspects of
the research process, including ethical
standards in research confidentiality and
an ability to protect intellectual property.

**must also demonstrate**

6. Up to 3 years of postdoctoral research
experience with evidence of developing
research independence; and demonstrated
ability to engage and actively collaborate
with a wide range of government and
scientific organisations or individuals.

**Remuneration and Benefits**
- The classification for this position is:
- Academic Level A which has an annual

remuneration range of $AUD83,894 to
$AUD113,836 pa. Which is inclusive of an
annual salary range of $AUD70,891 to
$AUD96,196 pa, 17% superannuation and
leave loading;
- Academic Level B which has an annual

remuneration range of $AUD119,829 to
$AUD142,312 pa. Which is inclusive of an
annual salary range of $AUD101,257 to
$AUD120,255 pa, 17% superannuation and
leave loading.

Beyond personal and professional fulfilment, a
- career at QUT brings a broad range of tangible
- benefits. With competitive remuneration
- including superannuation, the University offers
- real and generous benefits.
- QUT is a high quality and flexible organisation
- that is proud of its excellent employment
- conditions which include but are not limited to