Specialist research area: Machine Learning for IoT $29,863 p.a (full-time)

Overview

Australian Catholic University (ACU) is offering one (1) full-time, 3-year PhD scholarship to undertake research in the area of machine learning for Internet of Things (IoT). Specifically, the PhD research will focus on the intelligent modelling, classification, and annotation of multimodal IoT sensor data. The research is part of and funded by the Australian Research Council (ARC) Discovery Project titled ‘SenShaMart: A Trusted Internet of Things Marketplace for Sensor Sharing’ which also involves investigators from Swinburne University of Technology, CSIRO Data61, and University of Pittsburgh, USA. The research will be based in ACU’s Human-centred Intelligent Learning and Software Technologies research lab (HilstLab). 

About Australian Catholic University
According to the Times Higher Education World University Rankings 2022, ACU is proud to be positioned among the top universities in the world with a ranking of 251-300th that is the top 2 per cent of all universities worldwide. ACU is also ranked 39 among young universities in the world and one of the top 10 Catholic universities globally. In the 2018 Excellence in Research for Australia (ERA) assessment, research at ACU was ranked above (4/5) or well-above world-average (5/5) for ten fields of research in Australia. Research excellence at ACU is founded on the principles of social justice and attracts leading experts, students, and collaborators from across the globe.  
About HilstLab

HilstLab is an information technology research cluster within the Faculty of Law and Business, ACU. The research at HilstLab is centred around humans with an aim to develop and/or study applications that can solve real-world problems. With technological pillars of intelligent learning and advanced algorithms and software, HilstLab develops a range of industrial applications and study their impacts on user experience and task performance. Human-centredness means humans are not just users of a technological solution; they are a central part of the solution. HilstLab has expertise across domains of data science, machine learning, software engineering and human AI interaction, and provides access to powerful GPU servers and machines for conducting computationally intensive experiments such as big data analytics and deep learning.

About the PhD Stipend Scholarship

This PhD scholarship is offered in the fields of Machine learning (FoR code 4611) and Distributed computing and systems software (FoR code 4606). Built on our existing work in IoT sensor data classification and semantic annotation and time series analysis, the candidate will be exposed to real-world open IoT and time series datasets, practical ontology frameworks to model and describe the data, and importantly machine learning or deep learning techniques such as active learning, transfer learning, multimodal learning, graph-based learning, self-supervised learning, and few-shot or zero-shot learning, to tackle the IoT grand challenges of inferring or predicting from heterogenous, limitedly labelled, mislabelled/noisy, incomplete/missing, encrypted/anonymised open IoT data.

The Scholarship is available for a high-performing candidate, with prior experience in computer science research, data analytics, machine learning, deep learning, advanced data structure and algorithms and seeking to undertake research in the following broad areas:

  • Machine learning and AI
  • Distributed systems and IoT

We welcome applications from individuals with a solid background in:

  • Programming and data analytics
  • Machine learning and deep learning
  • Data structures and algorithms
PhD Eligibility criteria
Scholarship criteria

In addition to the PhD Eligibility criteria the successful applicant should:

  • Prior research experience in computer science, data analytics, machine learning or other relevant areas (with high quality CORE A*/A and SJR Q1 publications preferred)
  • Proficiency in Python programming and using its data science and machine learning libraries (experienced in PyTorch/TensorFlow/Keras is a plus)
  • Programming experience in Java or C++
  • Solid understanding on the theories behind machine learning, deep learning, and algorithms in general such as statistics, probability, and discrete maths (knowledge of IoT is also a plus)
Study mode and location
Commencement date 
  • For Onshore applicants: the scholarship is for commencement in Research Term A 2023.
  • For Offshore applicants: temporary offshore commencement may be possible in certain circumstances until visa is granted.
Value and duration

The Successful applicant will be awarded:

  • An Australian Research Council funded stipend scholarship of A$29,863 per annum (tax-free, indexed). The stipend duration is for three (3) years, subject to satisfactory progress and full-time study mode; plus
  • A Research Training Program Fees Offset Scholarship; that is, a tuition fee waiver for three (3) years for domestic candidates or an ACU tuition fee waiver for three (3) years for international candidates.
  • Single Overseas Student Health Cover (OSHC) for international applicants

In addition, paid casual teaching opportunities are available at our IT discipline subject to expertise and experience.

Application closes
  • 11:59pm (AEST) Monday 31 October 2022
Application instructions

This scholarship is open to high quality international applicants in addition to domestic candidates who are either a citizen or permanent resident of Australia, or a citizen of New Zealand.

Interested candidates are encouraged to contact Dr Kewen Liao before submitting an application for the PhD program and this scholarship:

E: kewen.liao@acu.edu.au

To be considered for these scholarships, applicants must submit all documentation required for an application to the PhD program at ACU, as detailed in the ‘How to Apply’ instructions on the ACU Research Web Page, plus:

  • A CV detailing relevant academic and practical experience;
  • A cover letter detailing demonstrated interest and/or experience in the program of research described.

For general candidature enquiries, email Candidature Services on res.cand@acu.edu.au, with the subject line ‘‘PhD Stipend Scholarship specialist research area: Machine Learning for IoT". 

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