Job Description

Opportunity

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This is an outstanding opportunity for a highly motivated PhD candidate interested in energy-effective 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.

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Job Details

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- Job No.: 696617
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- Location: Clayton campus
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- Employment Type: Full-time
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- Duration: Up to 3.5 years (full-time) for a PhD study
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Remuneration

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- A Research Living Allowance of $37,145 AUD per annum (2026 rate with annual indexation)
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- Faculty of Information Technology Tuition Fee Scholarship (for international students only)
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- Top-up scholarship of $10,000 per annum
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- FIT Candidature Funding of $4,000 for the duration of the candidature
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- Up to $1,265 from Monash Graduate Research Office as a one-off travel grant
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- Top-up government scholarship of $7,135 per annum
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- Travel support of up to $2,000 per annum for first author publications to top‐tier venues, provided by Pluralis
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Project

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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.

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The research objectives include:

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- Develop new decentralised orchestration mechanisms for large‐scale AI model training across heterogeneous and geo‐distributed infrastructure
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- Design energy‐aware scheduling, resource allocation, and workload placement algorithms for distributed AI training
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- Improve the communication efficiency, scalability,



and reliability of decentralised training frameworks
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- Evaluate decentralised AI training systems using real‐world workloads, GPU infrastructure, and industry‐relevant deployment scenarios
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- Generate open‐source frameworks, algorithms, benchmarks, and research outputs that support sustainable and scalable AI infrastructure
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Qualifications

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- 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.
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- 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.
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- Or a qualification or combination of qualifications and professional experience deemed equivalent by the Graduate Research Committee.
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- 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.
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- 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.
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EEO Statement

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

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Application Closing

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

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Contact

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Dr Mohammad Goudarzi – [email protected]

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