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Susquehanna International Group

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Machine Learning Internship Program – PhD Hire – NY/Philadelphia area Susquehanna International Group, LLP in United States

Degree Level

PhD

Field of study

Computer Science

Funding

Available

Deadline

Apr 13, 2027

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Country

United States

University

Susquehanna International Group

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Keywords

Computer Science
Data Science
Signal Processing
Mathematics
Predictive Modeling
Quantitative Finance
Artificial Neural Network
Statistics
Laboratory Experimentation
Physics
ML

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About this position

This internship offers PhD candidates a 10-week immersive research experience in Susquehanna International Group’s Quantitative Research environment. The project sits at the intersection of data, algorithms, and markets, with interns contributing to machine learning research that supports quantitative trading and finance.

Working with Susquehanna’s Machine Learning team, the intern will tackle high-impact problems using proprietary datasets and substantial computing infrastructure, including a large GPU cluster designed for intensive training, simulation, and rapid experimentation. The role emphasizes research on noisy, non-stationary data, experimental design, and the development of models that can inform predictive decisions and strategic execution.

The internship is embedded in a collaborative research culture where researchers, developers, and traders work closely together, ideas are debated openly, and validated work can move quickly toward production. Students also gain one-on-one mentorship and take part in a comprehensive education program focused on ML, quant research, and trading practices.

The work spans alpha generation, signal processing, model deployment, and risk-aware decision making, giving interns exposure to the practical use of advanced machine learning in systematic trading.

Funding details

Available

What's required

Currently pursuing a PhD in Computer Science, Machine Learning, Statistics, Physics, Applied Mathematics, or a closely related field. Applicants should have proven experience applying machine learning techniques in a professional or academic setting, a strong publication record in top-tier conferences such as NeurIPS, ICML, or ICLR, and hands-on experience with ML frameworks including PyTorch and TensorFlow. A deep interest in solving complex problems and thriving in a fast-paced, competitive environment is also sought.

How to apply

Apply through the AcademicJobsOnline application link provided. Submit your materials via the online portal before the deadline.

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