Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Susquehanna International Group
- Carnegie Mellon University
- The University of Chicago
- Cornell University
- Duke University
- Purdue University
- Nature Careers
- The University of Iowa
- University of Nebraska–Lincoln
- Arizona State University
- Binghamton University
- Brookhaven Lab
- Claremont Graduate University
- Columbia University
- Fermilab
- Institute for Basic Science
- Pennsylvania State University
- Radix Trading LLC
- Temple University
- The Ohio State University
- University of Florida
- University of Massachusetts Medical School
- University of Miami
- University of Minnesota
- University of Pennsylvania
- University of Pittsburgh
- University of South Carolina
- University of Texas at Austin
- University of Utah
- 19 more »
- « less
-
Field
-
, United States of America [map ] Appl Deadline: (posted 2025/06/24, listed until 2026/06/23) Position Description: Apply Position Description Overview Susquehanna is expanding the Machine Learning group and seeking
-
Operations Research Physics Appl Deadline: (posted 2025/06/24, listed until 2026/06/23) Position Description: Apply Position Description Overview Susquehanna is expanding the Machine Learning group and seeking
-
, data science using EHRs, machine learning, data mining, and natural language processing are preferred. Job Description: Develop large language models and other methods and tools to effectively use
-
strong research capabilities with a deep understanding of trading to design, validate, backtest, and implement statistical and advanced machine learning models. Your work will span a range of initiatives
-
capabilities with a deep understanding of trading to design, validate, backtest, and implement statistical and advanced machine learning models. Your work will span a range of initiatives, including large-scale
-
machine learning techniques to predict market behavior and generate alphas Execution. Create strategies to execute on modelling ideas under simulated competition Evaluation. Backtest ideas using historical
-
machine learning techniques to predict market behavior and generate alphas Execution. Create strategies to execute on modelling ideas under simulated competition Evaluation. Backtest ideas using historical
-
performance. What you can expect Modelling. Apply probability theory, statistical analysis, and machine learning techniques to analyze and interpret market behavior Alpha Monetization. Blend quantitative
-
, statistical analysis, and machine learning techniques to predict market behavior and generate alphas Execution. Create strategies to execute on modelling ideas under simulated competition Evaluation. Backtest
-
theory, statistical analysis, and machine learning techniques to predict market behavior and generate alphas Execution. Create strategies to execute on modelling ideas under simulated competition