PhD Scholarship - Energy-Efficient Decentralised Training Frameworks for Large-Scale AI Models on Geo-Distributed Infrastructure

#job-content { border: 1px solid #000000; } h3 { color:#000000; } .jobdets { padding:0px 25px; } PhD Scholarship - Energy-Efficient Decentralised Training Frameworks for Large-Scale AI Models on Geo-Distributed Infrastructure Job No.: 696617 Location: Clayton campus Employment Type: Full-time Duration: The scholarship may be held for up to 3.5 years (fulltime) for Research Doctorate (PhD) studies Remuneration: The successful applicant will receive A Research Living Allowance, at current value of $37,145 AUD per annum for PhD (2026 rate with annual indexation) Faculty of Information Technology Tuition Fee Scholarship (for international students only) Top-up scholarship of $10,000 per annum FIT Candidature Funding of $4,000 for the duration of the candidature Up to $1,265 from Monash Graduate Research Office as a one-off travel grant Top-up government scholarship $7,135 per annum Travel support of up to $2,000 per annum for first author publications to top-tier venues, provided by Pluralis The Opportunity This is an outstanding opportunity for a highly motivated PhD candidate interested in 𝐞𝐧𝐞𝐫𝐠𝐲-𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐝𝐞𝐜𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐬𝐞𝐝 𝐀𝐈 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠, 𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐬𝐲𝐬𝐭𝐞𝐦𝐬, and 𝐥𝐚𝐫𝐠𝐞-𝐬𝐜𝐚𝐥𝐞 𝐟𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐦𝐨𝐝𝐞𝐥𝐬. The successful candidate will be supervised by 𝐃𝐫 𝐌𝐨𝐡𝐚𝐦𝐦𝐚𝐝 𝐆𝐨𝐮𝐝𝐚𝐫𝐳𝐢 at 𝐌𝐨𝐧𝐚𝐬𝐡 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲, with co-supervision and support from leading academic and industry experts. The candidate will join the 𝐅𝐚𝐜𝐮𝐥𝐭𝐲 𝐨𝐟 𝐈𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 at Monash University and work closely with 𝐏𝐥𝐮𝐫𝐚𝐥𝐢𝐬 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡, the industry partner on this project. The project focuses on developing 𝐞𝐧𝐞𝐫𝐠𝐲-𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐝𝐞𝐜𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐬𝐞𝐝 𝐨𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 𝐦𝐞𝐜𝐡𝐚𝐧𝐢𝐬𝐦𝐬 𝐚𝐧𝐝 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 for training large-scale foundation models across 𝐠𝐞𝐨-𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞. This research addresses a critical challenge in modern AI: how to train increasingly large models in a more scalable, accessible, and sustainable way. As AI systems continue to grow, centralised training infrastructure creates significant barriers related to cost, energy consumption, infrastructure access, and environmental impact. This project will explore new decentralised training frameworks that can better utilise distributed computing resources while reducing energy overheads and supporting more sustainable AI infrastructure. The successful candidate will have the opportunity to work on real-world industry problems, access advanced computing infrastructure, collaborate with researchers and engineers at Pluralis Research, and contribute to high-impact research outputs, open-source tools, and potential commercial translation pathways. The project is especially suited to candidates with strong interests in 𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐬𝐲𝐬𝐭𝐞𝐦𝐬, 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐟𝐨𝐫 𝐀𝐈/𝐌𝐋, 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐬𝐲𝐬𝐭𝐞𝐦𝐬, 𝐫𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐨𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧, 𝐜𝐥𝐨𝐮𝐝/𝐞𝐝𝐠𝐞 𝐜𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠, and 𝐬𝐮𝐬𝐭𝐚𝐢𝐧𝐚𝐛𝐥𝐞 𝐀𝐈. Through this National Industry PhD project, the candidate will receive rigorous academic training, meaningful industry experience, and professional development support. They will be embedded with Pluralis Research for part of their candidature, gaining direct exposure to industry-scale decentralised AI training platforms and practical deployment challenges. Upon completion, the candidate will be well positioned for a leading career in academia, industry research, or advanced AI infrastructure development. To be considered for this opportunity you should fulfil the eligibility requirements listed below. The academic qualification requirements for this PhD is: A bachelor’s degree of at least four years in a relevant discipline, which includes a research thesis or project, with a minimum overall average grade of an honours degree equivalent to the First Class Honours; or A master's degree in a relevant discipline which includes a research thesis or project equivalent to at least 25 percent of one year of full-time study, with a minimum overall average grade of honours equivalent to the First Class Honours; or A qualification, or combination of qualifications and relevant professional experience, deemed equivalent by the GRC (or delegate). For this particular position, applicants must also have an undergraduate or postgraduate qualification in computer science, information technology, machine learning, artificial intelligence, software engineering, or a closely related discipline. Ideally, applicants will have training and research experience in one or more of the following areas: distributed systems, systems for AI/ML, machine learning systems, decentralised training, large-scale AI model training, resource orchestration, cloud/edge computing, high-performance computing, or energy-efficient computing. Monash University strongly advocates diversity, equality, fairness and openness . We fully support the gender equity principles of the Athena SWAN Charter . The Project We invite applications from outstanding PhD candidates with an undergraduate or postgraduate qualification in computer science, information technology, artificial intelligence, machine learning, software engineering, computer/electrical engineering, or a closely related discipline. This PhD project is part of a 𝐍𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐲 𝐏𝐡𝐃 project at 𝐌𝐨𝐧𝐚𝐬𝐡 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲, in collaboration with industry partner 𝐏𝐥𝐮𝐫𝐚𝐥𝐢𝐬 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡. The project aims to develop 𝐞𝐧𝐞𝐫𝐠𝐲-𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐝𝐞𝐜𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐬𝐞𝐝 𝐨𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 𝐦𝐞𝐜𝐡𝐚𝐧𝐢𝐬𝐦𝐬 𝐚𝐧𝐝 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 for training large-scale foundation models across 𝐠𝐞𝐨-𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞. Large-scale AI training is increasingly limited by high energy consumption, infrastructure cost, communication overhead, and reliance on centralised datacentres. This project will investigate how decentralised and geo-distributed computing resources can be orchestrated more efficiently to support scalable, sustainable, and accessible training of large AI models. The project will focus on one or more of the following research objectives: Develop new decentralised orchestration mechanisms for large-scale AI model training across heterogeneous and geo-distributed infrastructure; Design energy-aware scheduling, resource allocation, and workload placement algorithms for distributed AI training; Improve the communication efficiency, scalability, and reliability of decentralised training frameworks; Evaluate decentralised AI training systems using real-world workloads, GPU infrastructure, and industry-relevant deployment scenarios; and Generate open-source frameworks, algorithms, benchmarks, and research outputs that support sustainable and scalable AI infrastructure. The project is expected to generate new knowledge, tools, and practical frameworks for reducing the energy footprint of large-scale AI training while improving the accessibility and efficiency of AI infrastructure. Expected outcomes include novel decentralised training algorithms, energy-efficient orchestration techniques, open-source software artefacts, high-quality research publications, and potential translation pathways into industry systems. To Apply for the position, please check the below steps: Stage 1: 𝐅𝐢𝐥𝐥 𝐨𝐮𝐭 𝐭𝐡𝐞 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐟𝐨𝐫𝐦: lnkd.in/gphc2G7E Stage 2: Please email your application and documents to Dr Goudarzi’s Email address ' [email protected]' with the following title “[𝐏𝐫𝐨𝐬𝐩𝐞𝐜𝐭𝐢𝐯𝐞 𝐍𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐲 𝐏𝐡𝐃 𝐒𝐭𝐮𝐝𝐞𝐧𝐭] – [𝐘𝐨𝐮𝐫 𝐍𝐚𝐦𝐞]” Stage 3: Applications will be reviewed based on eligibility, academic performance, research background, publication record, and alignment with the project topic. Shortlisted candidates will receive an email regarding the interview process. Enquiries: Dr Mohammad Goudarzi, [email protected] Applications Close: Sunday 30 August 2026, 11:55pm AEST We will begin the interview process as soon as suitable applications are received, so applicants are encouraged to apply early. We will not wait until the closing date to start shortlisting and interviews. Supporting a diverse workforce Back to search results Email Job Monash University recognises that its Australian campuses are located on the unceded lands of the people of the Kulin nations, and pays its respects to their elders, past and present.