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Spatiotemporal Object Detection/Segmentation from Multi-Spectral Time Sequence Data

Primary supervisor

Hamid Rezatofighi

Co-supervisors

  • Prof. Uwe Aickelin, Faculty of Engineering and Information Technology, The University of Melbourne

Positions: We have an ARC fully-funded PhD project with generous top-up scholarship in the areas of machine learning and computer vision. The PhD project is 3.5 years, including at least a one-year equivalent industry placement, the timing of which can be negotiated.

The project’s research scope could include domain adaption, few-shot learning and/or active learning for spatiotemporal object detection/segmentation from multi-spectral time sequence data. Our aim is to develop a fully functional (and real-time) framework for our large-scale, imbalanced and long-tail dataset.

The research will be performed in The Australian Research Council (ARC) Training Centre in Optimisation Technologies, Integrated Methodologies, and Applications (OPTIMA) at Monash University (Clayton Campus, Melbourne Australia) and is in close collaboration with the University of Melbourne and an industry partner. The position is immediately available and will be filled as soon as a suitable candidate is found.

An expression of interest must be submitted here before prospective PhD students are invited to apply for admission.

Before applying, please familiarise yourself with Monash Universities admission requirements, as a PhD cannot be offered if you do not meet the universities' stringent requirements, Monash University (https://www.monash.edu/graduate-research/future-students/apply), Monash University will only process applications for OPTIMA students after you have completed this EOI, attended an industry interview and have been provided with an OPTIMA letter of acceptance.

Interested applicants are welcome to contact Dr Hamid Rezatofighi (hamid.rezatofighi@monash.edu) for more information.

About OPTIMA

OPTIMA is a partnership between the University of Melbourne, Monash University, three international universities, and 11 industry partners. The partners are in the advanced manufacturing, energy resources, and critical infrastructure sectors. They include Boeing Aerostructures AustraliaAGL EnergyIBM Australia and Melbourne Water.

OPTIMA works with the industry to conduct world-class research and provide training for research students working in industrial optimisation. OPTIMA addresses industry's urgent need for decision-making tools to support global competitiveness: reducing lead times and financial and environmental costs while improving efficiency, quality and agility.

Despite strong expertise in academia, the industry is yet to fully benefit from optimisation technology due to its high barrier to entry. Connecting industry partners with world-leading interdisciplinary researchers and talented students, OPTIMA will advance an industry-ready optimisation toolkit while training a new generation of industry practitioners and young researchers, vanguarding a highly skilled workforce of change agents for the transformation of priority sectors including advanced manufacturing, energy resources and critical infrastructure. 

Mission: Industrial transformation via increased uptake of trusted and sophisticated optimisation technologies.

Learn more about OPTIMA's industry partners.

For further information, please contact Charlotte Hurry, Centre Manager, at info@optima.org.au

 

Required knowledge

Your profile:

We are looking for a highly motivated candidate, who is eager to get involved in cutting edge, creative research with real-world applications.

Essential Skills:

  • You hold an Honour/Master degree in Computer Science, Mathematics, Physics, or Engineering.
  • A background in machine learning and computer vision.
  • You have excellent skills in areas such as applied mathematics, statistics/probability theory or computational maths/physics.
  • Programming skills in a variety of languages (e.g., Python, Matlab, C/C++/C#, CUDA).
  • We expect fluent communication skills in English.

Desirable Skills

  • You are already a proficient programmer in one of the main Deep Learning libraries (e.g., Darknet, TensorFlow, PyTorch, Keras).
  • Previous experience working with state-of-the-art computer vision or other machine learning/deep learning applications is also advantageous.
  • An understanding of signal/image processing and Fourier based analysis.

Project funding

Project based scholarship

Learn more about minimum entry requirements.