Royal Melbourne Institute of Technology
1 week ago
CSIRO Industry PhD Program
PhD in Risk-Aware and Trustworthy AI Agents for Adaptive Quantitative Trading Royal Melbourne Institute of Technology in Australia
Degree Level
PhD
Field of study
Computer Science
Funding
Full funding availableCountry
Australia
University
Royal Melbourne Institute of Technology

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About this position
PhD opportunity in Risk-Aware and Trustworthy AI Agents for Adaptive Quantitative Trading through the CSIRO Industry PhD Program.
The project focuses on building a coordinated multi-agent AI framework for adaptive quantitative trading in dynamic financial markets. Research themes include computer science, finance, machine learning, reinforcement learning, financial time-series modelling, explainable AI, risk management, and multi-agent systems. The aim is to support robust strategy execution, real-time risk governance, interpretable trade rationales, and accountable decision-making under market regime shifts and regulatory constraints.
Supervision involves Chao Chen at Royal Melbourne Institute of Technology, Mengmeng Yang at CSIRO, and Chong Wang at WX Wealthverse Pty Ltd.
The student will be based primarily at Royal Melbourne Institute of Technology, Melbourne, Australia, with industry engagement at WX Wealthverse Pty Ltd and additional activity at CSIRO Clayton.
Eligibility highlights: applicants should have a background in computer science, data science, mathematics, finance, or a related field; strong Python skills; solid knowledge of machine learning; and basic probability and statistics. Desirable experience includes PyTorch, reinforcement learning, time-series modelling, or multi-agent systems, plus interest in financial markets and explainable AI.
This opportunity is listed as open until position filled. Applications are submitted via the RMIT scholarship page.
Funding details
Full funding including tuition fees and living expenses is available for this position. The scholarship covers all educational costs and provides a monthly stipend.
How to apply
Please submit your application including a cover letter, CV, academic transcripts, and contact information for two references. Applications should be sent via the online portal before the deadline.
More information can be found here
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