PhD Quant Research Intern: Build Models for Live Markets

StudentJob; Amsterdam; On-site; EUR 13,000 - 20,000; Full time

Job summary

Graviton is seeking a Full‑time PhD Quantitative Research Intern for a 10–12 week period at our Amsterdam office. You will collaborate with researchers to investigate market behaviour, test hypotheses and build predictive models that translate research into live trading signals.

Ideal candidates are mathematicians, physicists, statisticians or computer scientists who want to solve hard problems in markets, with exposure to large datasets and high-performance infrastructure.

Qualifications

• Full‑time PhD student in a quantitative discipline such as mathematics, physics, computer science, statistics or a related field, graduating 2028
• Strong programming skills in Python and/or C++
• Depth in probability, statistics, linear algebra and optimization, with research experience in machine learning, statistical modelling or data analysis

Responsibilities

• Discover predictive patterns from billions of market events
• Design statistical models that identify trading opportunities
• Apply probability, optimization and machine learning to open research problems
• Run large-scale simulations and experiments on high-performance research infrastructure
• Design experiments and backtests to evaluate hypotheses
• Build AI-powered research agents that accelerate experimentation
• Develop infrastructure for rapid, large-scale research
• Translate research into production-ready trading signals
• Explore new ML, AI and quant finance techniques to find the next source of alpha

Skills

Python

C++

Probability

Statistics

Machine learning

Education

PhD candidate in quantitative discipline

Tools

Python

C++

Job description

Graviton is seeking a Full‑time PhD Quantitative Research Intern for a 10–12 week period at our Amsterdam office. You will collaborate with researchers to investigate market behaviour, test hypotheses and build predictive models that translate research into live trading signals.

Ideal candidates are mathematicians, physicists, statisticians or computer scientists who want to solve hard problems in markets, with exposure to large datasets and high-performance infrastructure.