Join IMC as a Quant Research Intern and work at the intersection of advanced machine learning techniques and high-impact, real-world trading strategies.  This 3-month internship, based in Sydney, is designed to give you the opportunity to apply your research skills in a fast-paced, innovative environment, where your contributions will directly inform our approach to high and mid-frequency trading strategies for the equities and futures markets.

As part of our team, you will work on developing predictive models using large-scale data analysis, pushing the boundaries of our research methodology. You will collaborate with a team of experienced researchers, traders, and engineers, all while receiving mentorship from a senior member of the team. The internship will commence in June 2026 and provides the chance to transition into a permanent Quant Researcher position with IMC upon successful completion of your PhD.

Due to the sensitive IP you will be exposed to within IMC trading, as is common in trading, it is highly unlikely any research findings will be suitable for outside academic publication.

Your Core Responsibilities: 

• Conduct hands-on research to design, develop, and apply original machine learning algorithms, with the autonomy to explore and innovate.
• You will have particular experience in at least one of the following techniques: Deep Neural Networks, Reinforcement Learning, Natural Language Processing (NLP): Time Series Analysis, Anomaly Detection and Pattern Recognition
• Architect, prototype, and refine predictive models and trading strategies for robustness and scalability in the equities and futures markets.
• Analyze large, complex datasets to uncover statistically significant patterns and market behavior insights.
• Communicate research progress and outcomes effectively, ensuring knowledge transfer within the team and across departments.
• Collaborate closely with a multidisciplinary team to enhance IMC’s machine learning capabilities and trading strategies.

Your Skills and Experience: 

• Currently pursuing a PhD (or nearing completion) in Computer Science, Electrical Engineering, Mathematics, Statistics, Physics, or related quantitative fields.
• Deep understanding of machine learning, probability, and statistics, with practical experience in areas such as time series analysis, pattern recognition, portfolio optimisation, market impact modelling and NLP.
• Proven ability to conduct original research and deliver impactful results.
• Extensive experience with modern deep learning architectures, with a solid foundation in classic machine learning techniques.
• Strong programming skills in Python (essential), with additional experience in C++ considered a plus.
• Familiarity with analyzing large, unstructured, and heterogeneous datasets.
• Theoretical and hands-on experience with time series forecasting is a plus.
• Alongside your resume, please include one to two papers which best demonstrate your research capabilities.