Dynamics of Diversity in Complex Systems: Entropy, Extinction and Innovation

Italian Ministry of Education, University and Research; Italia; On-site; EUR 32,000 - 52,000; Full time

Job summary

Sapienza Università di Roma seeks a researcher to study diversity in complex systems described by probability distributions evolving over time.

The project emphasizes entropy, distribution support, and divergences, with applications of neural networks and generative models to understand concentration and loss of diversity.

Qualifications

• Knowledge of statistical physics and stochastic processes.
• Familiarity with information theory and neural networks a plus.
• Experience in analysing complex systems and diversity measures.

Responsibilities

• Study diversity in complex systems described by probability distributions undergoing time evolution.
• Analyze conditions for conservation, reduction or loss of diversity focusing on entropy and distribution support.
• Apply methods from statistical mechanics and information theory to characterize stability and emergent configurations.

Skills

Statistical mechanics

Probability distributions

Machine learning

Job description

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: