Quant Finance Internships Australia

11 jobs

  1. IMC Trading

    Deep Learning Research Intern - 2027 - Sydney Sydney, Australia

    Sydney

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  2. Tower Research Capital

    Quantitative Researcher Intern, PhD or Postdoc

    Sydney

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  3. Tower Research Capital

    Quantitative Researcher Intern, Bachelor's or Master's

    Sydney

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  4. Jane Street

    Software Engineer

    Hong Kong, Hong Kong

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  5. Jane Street

    Quantitative Trader

    Hong Kong, Hong Kong

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  6. Jane Street

    Quantitative Researcher

    Hong Kong, Hong Kong

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  7. Jane Street

    Linux Engineer

    Hong Kong, Hong Kong

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  8. Jane Street

    IT Operations Engineer

    Hong Kong, Hong Kong

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  9. Jane Street

    Cybersecurity Engineer

    Hong Kong, Hong Kong

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  10. Jump Trading / Jump Crypto

    Campus Quantitative Researcher (Intern)

    Singapore

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  11. Five Rings

    Summer Intern 2027 - Quantitative Trader

    New York

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IMC Trading

Deep Learning Research Intern - 2027 - Sydney Sydney, Australia

Sydney

First seen

Eligibility

  • Enrolled in a doctoral (PhD) program.
  • Ability to work full-time for 10-12 weeks during the internship period.

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Apply on company site Full job page

A 10-12 week summer internship in Sydney for PhD students to conduct deep learning research supporting IMC's quantitative trading strategies, including mentorship and potential full-time offers.

Responsibilities

  • Conduct research and develop machine learning algorithms for market problems.
  • Analyze large-scale datasets and develop predictive models.
  • Participate in classroom and on-desk training about quantitative trading.
  • Receive mentorship and feedback from experienced researchers.

Requirements

  • Current PhD candidate in ML, CS, EE, Math, Stats, Physics, or related field.
  • Strong foundations in ML, probability, statistics, and hands-on research experience in deep learning.
  • Proficiency in Python and ML frameworks such as PyTorch or Tensorflow.
  • Demonstrated research excellence including publications or projects.

Skills

  • Deep learning frameworks (PyTorch, Tensorflow, JAX).
  • Machine learning theory and practice.
  • Research skills and experimentation.
  • Programming in Python.
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