Cubist PhD Fellowship

Cubist PhD Fellowship

The Cubist PhD Fellowship supports exceptional doctoral students in finance economics mathematics statistics physics electrical engineering or computer science and looks to advance their research.

PhD Fellows will be eligible to receive a grant of $100000 to fund their research. They will be paired with a Cubist mentor that has a background in a similar field who can offer guidance throughout the Fellowship.

Fellows will be invited to visit our New York office to present their research in an academic seminar to Cubist employees and will automatically be considered for 2026 summer internships at the firm.

If youre passionate about problem solving and conducting meaningful research that has real impact we encourage you to apply!

Eligibility:

• Full-time students currently pursuing a PhD in finance economics mathematics statistics physics electrical engineering or computer science at an accredited university in the Unites States or United Kingdom.
• Students must be in at least the third year of their PhD program when the Fellowship begins in Fall 2025.
• Students that are already supported by another industry fellowship are not eligible.

Award Details:

• Fellows will be awarded a $100000 grant.
• Fellowships will be paid directly to the university to cover tuition fees and living expense stipends. If accepted we will work with your program to arrange the award of your fellowship.

Applications should include:

• A CV or resume.
• A brief statement explaining their research (1-2 pages in length ideally). Please submit as the application cover letter.
• Applications are due by July 1^st. Results will be announced in August.

About Cubist

Cubist Systematic Strategies an affiliate of Point72 deploys systematic computer-driven trading strategies across multiple liquid asset classes including equities futures and foreign exchange. The core of our effort is rigorous research into a wide range of market anomalies fueled by our unparalleled access to a wide range of publicly available data sources.