|Entreprise/Organisme :||Ecole Polytechnique, France|
|Niveau d'études :||Doctorat|
|Date de début :||dès que possible|
|Durée du contrat :||3 ans|
|Rémunération :||2400€ nets mensuels dépendant de l'expérience|
|Secteur d'activité :||Stochastic Simulation, Uncertainty Quantification, Statistical modeling|
|Description :||Location. École Polytechnique is a French public institution of higher education and research in Palaiseau, in the southwest of Paris. It was established in 1794 by the mathematician Gaspard Monge during the French Revolution. It is one of the most prestigious and selective French grandes écoles. Polytechnique has several research laboratories operating in various scientific fields (physics, mathematics, computer science, economics, chemistry, biology, etc.).
Research program. The Chaire “Stress Testing” is a specific research program between Ecole Polytechnique and BNP Paribas, and is hosted at Polytechnique by the Center of Applied Mathematics https://portail.polytechnique.edu/cmap/en
This research project is part of an in-depth reflection on the increasingly sophisticated issues surrounding stress tests (under the impulse of the upcoming European Banking regulation). Simulation of extreme adverse scenarios is an important topic to better understand which critical configurations can lead to financial and systemic crises. These scenarios may depend on complex phenomena, for which we partially lack information, making the modeling incomplete and uncertain. Last, the data are multivariate and reflect the dependency between driving variables.
From the above observations, different lines of research are considered:
1) the generation of stress test and meta-modeling scenarios using machine learning 2) the quantification of uncertainties in risk metrics
3) modeling and estimation of multidimensional dependencies
Keywords: Bayesian Networks, copulas, dependent data, Deep Learning, Gaussian processes, machine learning, Markov Chain Monte Carlo, meta-modeling, multivariate statistics, rare event simulation, risk metrics, splitting methods, stochastic algorithms, stochastic and Bayesian optimization, uncertainty propagation
• A PhD in Probability and Statistics, or equivalent
• A proven track record in quality research, as evidenced by research publications in top scientific journals and conferences
• Solid working knowledge of some of the topics listed above (see keywords)
• An understanding of financial risks is desirable
• Exposure to development of numerical methods or data analysis (with Python, R) is desirable
Position: 3 years, extension is possible.
Although this is mainly a research position, there is the possibility of a small teaching load Net salary: about 2400€/month depending on the experience
How to Apply: Your application should include a Cover Letter, Resume, publications. With your application, we ask that you briefly outline your experience against the selection criteria in the position description.
Contact for application: email@example.com|
|En savoir plus :||https://drive.google.com/file/d/1vPBXCjCTzHh447pvphxSPH2zihYlF90z|