The CSIRO Industry PhD Program (iPhD) at Queensland University of Technology (QUT) offers a unique four-year research training opportunity for aspiring innovation leaders interested in the intersection of research and industry. This program is designed to develop advanced skills in distributed and edge intelligence for smart grids, focusing on distributed energy resources and adaptive demand response management. The project aims to advance distributed algorithms for managing energy resources at customer premises, contributing to more resilient, secure, private, and responsive smart grid operations. Successful candidates will be supervised by a multidisciplinary team from QUT (Professor Raja Jurdak), CSIRO (Abdelwahed Khamis and Volkan Dedeoglu), and Energy Queensland Ltd (Kieren Astin-Walmsley), ensuring strong academic and industry engagement. The program includes a generous scholarship package of approximately $47,000 per annum (tax exempt, 2025 rate), a Research Training Program (RTP) Fee-Offset, and an additional $13,000 per annum for project expenses and professional development. Students will also participate in a three-month industry engagement with Energy Queensland Ltd and benefit from structured professional development activities. Eligibility is restricted to Australian citizens, Permanent Residents, or New Zealand citizens who meet QUT's PhD entry requirements, including English language proficiency. Applicants must not have previously completed a PhD and should be able to commence the program in the year of the offer. Full-time enrolment is expected, though part-time arrangements may be considered. Candidates must be willing to undergo onboarding and background checks for both CSIRO and Energy Queensland Ltd, and be prepared to work at approved project locations. Desirable skills include experience in programming (C, Python, or similar) and knowledge of machine learning or distributed systems. The application process involves emailing Professor Raja Jurdak with your academic background, motivation, and CV. If nominated, you will submit an expression of interest (EOI) to QUT, naming Professor Jurdak as your principal supervisor and referencing the scholarship link. Shortlisted candidates will be interviewed, and the final selection will proceed through QUT's formal PhD application process. This program offers a standard PhD upon completion and is an excellent opportunity for candidates interested in applied research, smart grid technologies, and industry collaboration. For more information and to apply, visit the official scholarship page.

This PhD opportunity focuses on intelligent edge computing for adaptive demand response management within smart grids, as part of the CSIRO Industry PhD Program. The project aims to advance distributed and edge intelligence for managing distributed energy resources, with the goal of achieving more resilient, secure, private, and responsive smart grid operations. Research will involve developing distributed algorithms and leveraging machine learning and edge AI to optimize energy management at the customer level. The successful candidate will be admitted to the Queensland University of Technology (QUT) PhD program and will be supervised by a team from QUT, CSIRO Data61, and Energy Queensland. The supervisory team includes Professor Raja Jurdak (QUT), Dr Abdelwahed Khamis (CSIRO Data61), Dr Volkan Dedeoglu (CSIRO Data61), and Kieren Astin-Walmsley (Energy Queensland). The primary research location is QUT's Gardens Point Campus in Brisbane, with industry engagement at Energy Queensland and additional collaboration with CSIRO Pullenvale. The scholarship package is valued at approximately $47,000 per annum (tax exempt, 2025 rate) for four years, with an additional $13,000 per annum for project expenses and development. The program includes a 60-day industry engagement component and structured professional development and training to enhance applied research skills. Eligibility is limited to Australian citizens, permanent residents, or New Zealand citizens. Applicants should have experience in programming (C, Python, or similar), and knowledge of machine learning or distributed systems is desirable. Candidates must be prepared to complete onboarding at Energy Queensland, including confidentiality agreements and other requirements. To apply, contact Professor Raja Jurdak. The position remains open until filled. For more information, visit the project page or reach out to the supervisors via the provided email addresses.