At a Glance
- Tasks: Engage in a meaningful research project using maths and computational analysis on financial data.
- Company: G-Research, a leading quantitative research firm in London.
- Benefits: Gain hands-on experience, mentorship, and the chance to present to senior management.
- Other info: 10-week summer programme from 21 June to 27 August 2027.
- Why this job: Perfect opportunity to apply your skills and make an impact in finance.
- Qualifications: Ideal for Masters or PhD students in quantitative fields with strong maths skills.
G-Research is offering a 10-week Summer Research Programme in London for aspiring Quantitative Researchers. From 21 June to 27 August 2027, you will tackle a meaningful research project applying mathematical and computational analysis to financial time-series.
You will be mentored by a Quantitative Researcher, with reviews and a final presentation to senior management. Ideal candidates are in the final or penultimate year of a Masters or PhD in a quantitative field, with a strong math background
#J-18808-LjbffrQuant Research Summer Intern - London, Accommodation employer: Braunford LLP
G-Research is an exceptional employer that fosters a collaborative and innovative work culture, providing NLP interns with invaluable mentorship and hands-on experience in quantitative finance research. Located in London, interns benefit from access to cutting-edge technology and the opportunity to present their findings to senior management, paving the way for potential full-time roles post-graduation.
StudySmarter Expert Advice🤫
We think this is how you could land Quant Research Summer Intern - London, Accommodation
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We think you need these skills to ace Quant Research Summer Intern - London, Accommodation
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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