Projet de recherche commun entre l’Université Paris Dauphine - PSL, l'École Polytechnique, le Centre de Recherche en Economie et en Statistique (CREST) et la R&D du groupe EDF.
Créé en même temps que la Chaire Dauphine Ecole Polytechnique EDF - CA CIB« Finance et Développement Durable – Approches Quantitatives », il a pour vocation d’accueillir des chercheurs de toutes institutions académiques désireux de travailler avec des ingénieurs-chercheurs de la R&D d'EDF sur les problématiques d’économie mathématique et de finance quantitative de long terme du secteur énergétique.
New Postdoctoral Position – Storage Optimization in Model-Free Settings
A postdoctoral position is now open at Inria (Paris) in collaboration with EDF on the topic "Storage Optimization in Model-Free Settings." The project aims to develop novel data-driven approaches for the optimal management of energy storage assets (gas storage, pumped hydro, and batteries), with a particular focus on model-free optimization and reinforcement learning. Candidates should hold (or be close to completing) a PhD in stochastic optimization, machine learning, or a closely related field. Applications will be reviewed on a rolling basis.
Further details, including the research project, application requirements, and contact information, are available in this document.
Open-source stochastic optimization library
The STochastic OPTimization library (StOpt) aims at providing tools in C++ for solving some stochastic optimization problems encountered in finance or in the industry. A python binding is available for some C++ objects provided permitting to easily solve an optimization problem by regression.
Different methods are available : dynamic programming methods based on Monte Carlo with regressions (global, local and sparse regressors), for underlying states following; some uncontrolled Stochastic Differential Equations ; Semi-Lagrangian methods for Hamilton Jacobi Bellman general equations for underlying states following some controlled Stochastic Differential Equations and Stochastic Dual Dynamic Programming methods to deal with stochastic stocks management problems in high dimension
