Fully funded doctoral position in multi-agent modelling of financial markets and cognitive computational neuroscience. Laboratory for Cognitive and Computational Neuroscience at the Ecole Normale Superieure Paris and the Chair for Quantitative Finance at CentraleSupelec are looking for exceptional candidates to work on a doctorate as a part of a collaborative project spanning development of multi-agent models of stock markets (e.g. see Lussange et al 2020, Bavard et al 2018, da Gamma Battista 2017) and the effects of human cognitive traits and bounded rationality on the market dynamics. The candidate will be physically based at ENS. The student will be advised by Damien Challet (Chair for Quantative Finance, CentraleSupelec) and Boris Gutkin (Mathematics of Neural Circuits Team, LNC2, ENS) and collaborate closely with Stefano Palminteri (Human Reinforcement Learning Team, LNC2, ENS). The student will have an opportunity to be involved in all aspects of the project, from computational modelling to behavioral experiments to data mining. Industrial applications of the project outcomes can also be explored as well as a potential collaboration with Capital Fund Management, Paris. More information can be found at: https://psl.eu/recherche/grands-projets-de-recherche/projets-europeens/progr... <https://psl.eu/recherche/grands-projets-de-recherche/projets-europeens/programme-doctoral-ai4thesciences> https://euraxess.ec.europa.eu/jobs/580403 <https://euraxess.ec.europa.eu/jobs/580403> Start expected in fall 2021. Elibigibility: Master’s degree or equivalent in a quantitative discipline. There is no nationality or age criteria, but applicants must not have resided or carried out their main activity (work, studies, etc.) in France for more than 12 months in the 3 years immediately before the deadline of the call Interested candidates should contact boris.gutkin@ens.fr <mailto:boris.gutkin@ens.fr> before January 30 to discuss their application to the program Bavard S, *Lebreton M, Khamassi M, Coricelli G, Palminteri S. Reference-point centering and range-adaptation enhance human reinforcement learning at the cost of irrational preferences <https://www.google.com/url?q=https%3A%2F%2Fwww.nature.com%2Farticles%2Fs41467-018-06781-2%3Ffbclid%3DIwAR2PuJYq4j97HoduM91T1UQrFY3fZr7mgYrgs-0x4Z8YuHfhK_hVJGZ3Y0c&sa=D&sntz=1&usg=AFQjCNGHi4yGhQzukv3Ic8LIC6_IsewCNg>. Nature Communications (2018). da Gama Batista, Joao, Domenico Massaro, Jean-Philippe Bouchaud, Damien Challet, and Cars Hommes. "Do investors trade too much? A laboratory experiment." Journal of Economic Behavior & Organization 140 (2017): 18-34. Lussange, J., Lazarevich, I., Bourgeois-Gironde, S., Palminteri, S., Gutkin, B. (2020) Modelling Stock Markets by Multi-agent Reinforcement Learning. Comput Econ. https://doi.org/10.1007/s10614-020-10038- <https://doi.org/10.1007/s10614-020-10038->w
