Internship - Generative Modeling for Synthetic Time-series Via Stochastic Interpolant
il y a 2 semaines
Paris, 75, FR- **ABOUT CFM**:
- We value innovation, dedication, collaboration, and the ability to make an impact. Together, we create a stimulating environment for talented and passionate experts in research, technology, and business to explore new ideas and challenge existing assumptions.
Summary
- Build and stress-test generative models based on flow matching/stochastic interpolants for synthetic time-series. You will create controllable synthetic datasets (with tunable noise, nonlinearity, regime changes, and exogenous controls) and/or adapt benchmarks from the literature, compare neural architectures (MLPs, Transformers, etc.) and training strategies, and quantify:
- Sample complexity and data efficiency across architectures
Whether learning a full conditional distribution improves accuracy when we only care about conditional means
Out-of-support generalization with control variables (how far can we extrapolate?)
- Motivation
- Diffusion-style generative models (flow matching / stochastic interpolants) work remarkably well for images and text, but their behavior on structured time-series—especially with controls and mixed scalar/sequence inputs—is less well understood. Also, in realistic settings we are in a limited data regime, which is not encountered in vision or NLP. This project asks: which architectures and training choices are most data-efficient, when is generative modeling worth it versus simple regressions, and how robust are these models to shifts in inputs statistics?
These are the main questions of interest; the internship will prioritize a subset based on progress and the intern’s interests.
Prerequisites: comfort with Python/PyTorch and basic probability/ML. Curiosity about generative modeling and careful experimental design.
** EQUAL OPPORTUNITIES STATEMENT**:
- We are continuously striving to be an equal opportunity employer and we prohibit any discrimination based on sex, disability, origin, sexual orientation, gender identity, age, race, or religion. We believe that our diversity, breadth of experience, and multiple points of view are among the leading factors in our success.
CFM is a signatory of the Women Empowerment Principles.
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Paris, France Capital Fund Management Temps plein**Date**:30 oct. 2025 **Lieu**: Paris, 75, FR **Entreprise**:Capital Fund Management **À PROPOS DE CFM**: Fondés en 1991, nous sommes une société mondiale de gestion d’actifs quantitative et systématique appliquant une approche scientifique à la finance pour développer des stratégies d’investissement alternatives pour nos clients. Nous...
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