Opportunity

This is an outstanding opportunity for a highly motivated PhD candidate interested in energy-efficient decentralised training, distributed systems, and large-scale AI 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.

Job Details

- Job No.:
- Location: Clayton campus
- Employment Type: Full-time
- Duration: Up to 3.5 years (full time) for a PhD study

Remuneration

- A Research Living Allowance of $37,145 AUD per annum (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 of $7,135 per annum
- Travel support of up to $2,000 per annum for first author publications to top‑tier venues, provided by Pluralis

Project

The project focuses on developing energy-efficient decentralised orchestration mechanisms and algorithms for training large-scale foundation models across geo‑distributed infrastructure. It addresses the challenge of training increasingly large models in a more scalable, accessible, and sustainable way.

The research objectives include:

- 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
- Generate open‑source frameworks, algorithms, benchmarks, and research outputs that support sustainable and scalable AI infrastructure

Qualifications

- Bachelor’s degree of at least four years in a relevant discipline, including a research thesis or project, with a minimum overall average grade of an honours degree equivalent to First Class Honours.
- Master’s degree in a relevant discipline with a research thesis or project equivalent to at least 25% of one year of full‑time study, with a minimum overall average grade of honours equivalent to First Class Honours.
- Or a qualification or combination of qualifications and professional experience deemed equivalent by the Graduate Research Committee.
- Specific to this position, applicants must hold an undergraduate or postgraduate qualification in computer science, information technology, machine learning, artificial intelligence, software engineering, or a closely related discipline.
- Ideally, candidate training and research experience in one or more of: 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.

EEO Statement

Monash University strongly advocates diversity, equality, fairness and openness. We fully support the gender equity principles of the Athena SWAN Charter.

Application Closing

Applications close on Sunday 30 August 2026, 11:55 pm AEST.

Contact

Dr Mohammad Goudarzi –

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📌 Energy-Efficient Decentralised Training Frameworks for Large-Scale AI Models on Geo-Distributed[...] (Melbourne)
🏢 Monash University
📍 Melbourne