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At Susquehanna, we approach quantitative finance with a deep commitment to scientific rigor and innovation. Our research leverages vast and diverse datasets, applying cutting-edge machine learning at scale to uncover actionable insights—driving data-informed decisions from predictive modeling to strategic execution. We are launching a 12–18 month fully funded faculty fellowship. This is a unique opportunity to pursue advanced machine learning research in a fast-paced, real-world environment—collaborating with teams at the frontier of quantitative trading.
Job Responsibility:
Conduct applied machine learning research using large-scale, real-world financial datasets
Develop novel modeling techniques and adapt state-of-the-art algorithms to unique challenges in quantitative finance
Collaborate with researchers and engineers to translate theoretical insights into production-scale systems
Contribute to the design of robust, high-performance ML infrastructure
Explore research directions aligned with your interests, with flexibility in scope and duration
Evaluate ideas in an industrial setting, generating insights that may inform future academic or applied work
Help grow our research community by fostering collaboration and leveraging your network within the ML and academic ecosystems
Requirements:
Exceptional faculty (tenured or tenure-track) with expertise in machine learning, deep learning, LLM, statistics, computer science, physics, applied mathematics, or related fields
Exceptional newly minted PhDs or postdocs developing a research agenda in machine learning, deep learning, LLM, statistics, computer science, physics, applied mathematics, or related fields
A strong theoretical foundation in ML and a passion for solving practical, open-ended problems
Strong programming skills (Python preferred)
experience with ML frameworks like PyTorch, TensorFlow or Jax
Intellectual curiosity, adaptability, and a collaborative mindset