The University of Camerino invites expressions of interest from postdoctoral researchers wishing to apply for the Marie Skłodowska-Curie Actions Postdoctoral Fellowships 2026 in the field of computational physics, quantum simulation, and machine learning. This research opportunity is hosted by Prof. Sebastiano Pilati at the School of Science and Technology and is developed within the Complex Quantum Matter (CQM) group. The project focuses on integrating advanced computational methods and deep learning techniques to address complex problems in quantum many-body systems and quantum simulation platforms.

Supervisor

Prof. Sebastiano Pilati is a researcher in computational physics at the University of Camerino, working within the Physics Division and the Complex Quantum Matter group. His research focuses on quantum many-body systems, ultracold atoms, and quantum computing, with particular attention to the development of quantum Monte Carlo algorithms and deep learning methods for complex quantum problems. His scientific activity spans computational approaches to quantum systems and the integration of machine learning techniques into physics simulations, contributing to advances in quantum simulation and quantum-enhanced computation.

Research Group and Facilities

The project will be carried out within the Complex Quantum Matter (CQM) group, which provides access to advanced computational and experimental infrastructures.

Facilities include:

• high-performance computing (HPC) multicore servers equipped with GPUs
• access to national and European supercomputing infrastructures, including CINECA (Leonardo cluster) and LUMI
• access to experimental laboratories focused on the transport properties of superconducting materials

The research group operates within an active international network, collaborating with institutions such as the University of Antwerp, the University of Barcelona, and the Pitaevskii Center for Bose-Einstein Condensation in Trento. The group is also involved in international research initiatives, including the National Quantum Science and Technology Institute (NQSTI) and international research networks on quantum systems.

Research Topic and Project Idea

The project aims to integrate deep learning techniques with conventional computational methods in order to enhance the simulation of complex quantum matter. It focuses on models relevant to quantum simulation platforms, including Rydberg atoms, trapped ions, ultracold atomic gases, and superconducting quantum computers, with the long-term objective of developing hybrid classical–quantum computational frameworks capable of addressing computationally challenging problems in condensed matter physics and related fields.

Candidate Profile and Career Development

The position is intended for candidates with a background in computational and condensed matter physics, with an interest in deep learning methods and high-performance computing. During the fellowship, the researcher will develop advanced competences in the integration of machine learning techniques with stochastic computational algorithms and quantum simulation platforms, strengthening their profile in a highly specialized and rapidly evolving research area.

Contact

Prof. Sebastiano Pilati - sebastiano.pilati@unicam.it