Posted May 8, 2026
Eligible candidates: Final-year PhD students or postdoctoral researchers
Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology, and trading expertise has shaped our collaborative mindset which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors. Over the years, QRT has invested in a global research and execution platform which has been deployed to cover all geographies and asset classes. This platform covers a broad spectrum from high to low frequency trading systems. Our culture is centred around technology, automation, and industrialized processes. We operate in multiple languages from C++ to Python and embrace open-source software. Your future role within QRT
You will join a team of quantitative researchers to learn how to design trading algorithms in a real-world data environment – a fast track to become a seasoned quantitative researcher. Surrounded by peers who have successfully transitioned from academia to applied research, you will receive mentorship from experienced professionals who will help you translate your theoretical expertise into real-world impact. The research environment at QRT is designed to be collaborative and intellectually stimulating. Within one of QRT's systematic teams - spanning high, mid, and low-frequencies - your core objective will be to develop high-quality predictive signals:
Leverage access to vast and diverse datasets to identify hidden statistical patterns and market opportunities. - Collaborate with fellow researchers to exchange ideas and refine methodologies. - Translate theoretical models into production-ready signals. - Lead the full research cycle - from idea generation to implementation. Your present skillset
Holding or pursuing a PhD (final year) degree in a quantitative field such as statistics, mathematics, physics, biology, computer science, or engineering. - A pragmatic attitude towards translating theoretical models into real-world data problems. - Proficiency in Python (preferred) or another leading programming language such as R, MATLAB, C++, or C#. - Experience working with large datasets across multiple time frames (a plus). - Ability to multitask in a fast-paced environment with attention to detail. - Intellectual curiosity to explore new data, solve complex problems, and connect ideas across disciplines. - Ability to work autonomously, in a collegial and collaborative setting, and with colleagues from diverse backgrounds and areas of expertise. - Strong communication skills. - Fluency in English (additional languages are a plus).
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