Mohammad Goudarzi

PhD Opportunity in Distributed Systems and Machine Learning at Monash University Dr. Mohammad Goudarzi, Assistant Professor at Monash University, is recruiting dedicated and talented PhD candidates for a fully funded position in the Faculty of Information Technology. The research areas include distributed systems, systems for machine learning, and machine learning for systems, with a focus on impactful and innovative projects. This is an excellent opportunity to join a pioneering research group and contribute to cutting-edge advancements in computer science and information technology. Research Areas: Distributed Systems, Cloud/Edge Computing, Systems for ML, ML for Systems, Applied Machine Learning. Eligibility: Applicants must have a WAM of 85+, at least one publication in top-tier journals (CORE A*/A or JCR Q1) or conferences (CORE A*/A), and be prepared for an interview. Candidates should be motivated, intellectually curious, and passionate about research and innovation in the relevant fields. Funding: The PhD position is fully funded, covering tuition and providing a stipend. The exact stipend amount is not specified, but the opportunity is described as fully funded for the duration of the PhD. Application Process: Interested candidates should fill out the provided Google Form and email their application, including a CV (with educational background, WAM for each degree, and research outputs) and academic transcripts, to Dr. Goudarzi at [email protected]. The subject line should be: “[Prospective PhD Student] – [Your Name]”. Deadlines: Internal faculty application deadlines are March 1st, 2026 (Round 1) and August 1st, 2026 (Round 3). Interviews will be conducted before these deadlines. Contact: After reviewing your documents, Dr. Goudarzi will reach out if you meet the criteria to start the interview process. For more details, visit the provided LinkedIn and application links. This is a unique chance to advance your academic career in distributed systems and machine learning at Monash University, Australia.

Monash University is advertising a fully funded Industry PhD opportunity in Energy-Efficient Decentralised Training Frameworks for Large-Scale AI Models on Geo-Distributed Infrastructure . The project sits in the Faculty of Information Technology and is run in collaboration with industry partner Pluralis Research . The research focus is on designing and developing energy-efficient decentralised orchestration mechanisms and algorithms for training large-scale foundation models across geo-distributed infrastructure . This is a strong fit for applicants interested in distributed systems , cloud computing , edge computing , machine learning , and AI infrastructure. Funding includes an annual stipend of approximately $50K per year (indexed annually), a tuition fee waiver for international students, travel support for conferences and workshops, and full health insurance . The post also highlights access to mentoring, research resources, and industry collaboration opportunities. Eligibility is open to domestic and international students . Applicants should have a WAM of 85+ and at least one relevant publication in top venues such as CORE A*/A or CCF Rank A conferences/journals. Candidates are asked to provide their CV, educational background, WAM for each degree, published research outputs, and academic transcripts. To apply, complete the linked form and email your application to [email protected] with the specified subject line. Selected candidates will be contacted for the interview process.

Monash University is advertising 2 fully funded PhD positions in an interdisciplinary cohort spanning the Faculty of Information Technology, the Faculty of Engineering, and industry partner NVIDIA , supported by the Monash AI Institute . The projects sit at the intersection of computer science , robotics , distributed systems , machine learning , and edge intelligence . The two linked PhD topics are: (1) context-aware AI optimisation within autonomous robots , focusing on efficient ML and neuro-symbolic methods that let robots adapt to contextual information and mission conditions; and (2) distributed orchestration across robots, coordinators, and robot teams , focusing on methods for coordinating AI execution within robots, between robots and a central orchestrator, and across multi-robot teams. The cohort includes Mohammad Goudarzi, Hamid Rezatofighi, Dana Kulic, and Trung Pham. The post highlights close collaboration with academic and industry supervisors, access to cutting-edge research, mentoring, and collaborative opportunities. Funding includes an annual stipend of about $37K per year (indexed annually), a tuition fee waiver for international students , travel support for conferences and workshops, and full health insurance . Eligibility : domestic and international students may apply, subject to Monash PhD admission criteria. Applicants should have a strong background in AI/ML, robotics, distributed systems, systems for AI/ML, or related areas, and at least one relevant publication in top conferences or journals (CORE A*/A, CCF Rank A). Shortlisted candidates will be invited to interview. How to apply : complete the Google Form and email the application to [email protected] with the specified subject line, CV, and transcripts. The post also links to general admission and eligibility information.