Descrizione dell'offerta
Organisation/Company SAPIENZA UNVIERSITA' DI ROMA Research Field Physics » Statistical physics Researcher Profile Recognised Researcher (R2) Leading Researcher (R4) First Stage Researcher (R1) Established Researcher (R3) Application Deadline 9 Nov 2026 - 23:59 (UTC) Country Italy Type of Contract To be defined Job Status Not Applicable Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No
Offer Description
The research activity will focus on the study of diversity in complex systems described by discrete or continuous probability distributions undergoing time evolution. It will analyze the conditions determining the conservation, reduction, or irreversible loss of diversity, with particular attention to the role of entropy, distribution support, statistical divergences, rare events, and extinction times. The project will leverage tools from statistical mechanics, information theory, stochastic processes, population dynamics, and neural networks. Within this framework, machine learning systems and artificial generative models will also be considered, where iterative generation and learning dynamics can produce concentration of the learned distribution, loss of support, and statistical collapse. The objective is to define quantitative models and observables to characterize the stability of diversity and the emergence of new configurations. Italy Eligibility of fellows: country/ies of residence: EUROPE Eligibility of fellows: nationality/ies:
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The research activity will focus on the study of diversity in complex systems described by discrete or continuous probability distributions undergoing time evolution. It will analyze the conditions determining the conservation, reduction, or irreversible loss of diversity, with particular attention to the role of entropy, distribution support, statistical divergences, rare events, and extinction times. The project will leverage tools from statistical mechanics, information theory, stochastic processes, population dynamics, and neural networks. Within this framework, machine learning systems and artificial generative models will also be considered, where iterative generation and learning dynamics can produce concentration of the learned distribution, loss of support, and statistical collapse. The objective is to define quantitative models and observables to characterize the stability of diversity and the emergence of new configurations. Italy Eligibility of fellows: country/ies of residence: EUROPE Eligibility of fellows: nationality/ies:
#J-18808-Ljbffr
Candidatura e Ritorno (in fondo)
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