RMIT-CSIRO Next-Gen PhD in Autonomous IoT Robot for Solar Panels Detection and Cleaning

Data61/CSIRO Next Generation Artificial Intelligence Graduates program and Applied Solar Energy will provide a PhD stipend scholarship for 3.5 years of $40,500 per annum.

The program will also supply $5,000 per annum for training expenses and $5,000 per annum for travel expenses for the candidate. The program will further provide each candidate with a 6-month industry placement opportunity.

Open now. 

The application will close once the position is filled.

One scholarship available. 

Applicants must have a first-class or 2A honours degree in Mechanical/ Electronics/Mechatronics Engineering, Computer Science or equivalent in a related discipline. The successful candidate is expected to demonstrate familiarity with machine learning methods, especially deep learning models and artificial neural networks.

Applicants must satisfy RMIT University’s Master by Research entry requirements.

Applicants must be Australian Citizens or Permanent Residents.

Interested applicants should email Dr Hamid Khayyam hamid.khayyam@rmit.edu.au with the following information.

1. Cover letter outlining interest and alignment with the proposed research, and addressing the eligibility criteria (also see "Further information below").
2. CV (including the names of two referees).
3. Copy of Honours/Masters thesis and/or first-authored published peer-reviewed conference/journal paper(s).

Shortlisted applicants will then be invited to an interview. The selected applicant will be supported to develop a formal PhD application to RMIT. A scholarship application will only be successful if the prospective candidate is provided with an offer for admission from RMIT University.

The successful candidate will need to demonstrate:

• Excellent written and verbal communication skills. with industry experiences.
• Strong computational, programming, algorithms, and data analysis skills.
• Outstanding research skills
• Capacity to work independently and as a part of a team.

Applicants with the following knowledge and experience are highly preferred:

• Mechanical, Electrical, or Mechatronics Engineering in particular experience in developing solutions by using machine learning for user behavior analysis based on data mining.
• A track record of published (or at least submitted) research articles.

Dr Hamid Khayyam: hamid.khayyam@rmit.edu.au