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Mahmoud Osman

@mahmoudosman

Quantitative researcher at LPNHE, Sorbonne Université / CNRS, who built a Python pipeline for Bayesian inference on noisy supernova data.

France

Message

What I'm looking for

I’m seeking a quantitative research role applying rigorous statistical modelling and signal research to systematic trading.

At LPNHE, Sorbonne Université / CNRS, I built and validated a Python pipeline for statistical modelling of more than 10,000 measurements of about 3,000 Type Ia supernovae.

I used Bayesian field-level inference to infer a high-dimensional latent field from sparse, noisy data, with uncertainty quantification. I also designed regularisation and error models to control overfitting and propagate uncertainty.

For the ZTF international collaboration, I contributed to integrating NaCl into the LEMAITRE cosmology pipeline and validated it through simulation data challenges. I generated simulated datasets to assess model bias and robustness.

In an ongoing independent quantitative research project, I’m evaluating momentum, short-term reversal, and volatility signals on the S&P 500, reporting uncertainty and avoiding look-ahead. I’m seeking a quantitative research role applying rigorous statistical modelling and signal research to systematic trading.

Experience

Work history, roles, and key accomplishments

Teaching Assistant

Sorbonne Université

Jan 2024 - Jan 2026 (2 years)

Delivered tutorials in mathematics and numerical physics, communicating complex quantitative concepts clearly.

PhD Researcher

LPNHE, Sorbonne Université / CNRS

Jan 2023 - Jan 2026 (3 years)

Built and validated an end-to-end statistical modelling pipeline in Python for Type Ia supernovae data, and inferred high-dimensional latent fields using Bayesian field-level inference. Contributed to the ZTF international collaboration and ran large-scale computations on HPC clusters.

Research Intern

LPSC

Jan 2022 - Dec 2022 (11 months)

Developed and tested statistical methods for mass-richness scaling relations of galaxy clusters, and presented results and recommendations to the research team.