DTP | Doctoral Training Programme 2023
AB INITIO METHODS AND EMERGING TECHNOLOGIES FOR NUCLEAR STRUCTURE
The 2023 ECT* Doctoral Training Programme edition focuses on ab initio nuclear theory, with emphasis on modern computational methods and emerging technologies. The past decade has seen considerable progress in this field, leading to fully fledged computations with three-nucleon forces in medium mass isotopes. High-performance computing is now pivotal to the quest for reaching predictions of complex and heavy isotopes. Future frontiers will exploit Machine Learning and Quantum Computing algorithms as tools for many-body nuclear physics.
The aim of the DTP 2023 is to provide the participants with a pedagogical introduction to many-body theories that allow a deep understanding of nuclear structure, present the open challenges in relation to modelling nuclear reactions and interaction with weak probes, and providing know how for implementation with high-performance and GPU computing. By the end of the program, the participants are expected to have a thorough understanding of current challenges in nuclear structure, have the background knowledge to grasp novel opportunities in the field, and should be equipped with the numerical tools to make their own contributions in tackling open problems.
PROGRAMME COORDINATORS
Carlo Barbieri, University of Milan and INFN sezione di Milano
Alessandro Roggero, University of Trento and INFN-TIFPA
STUDENT COORDINATOR AND ADVISOR
Alessandro Roggero, University of Trento and INFN-TIFPA
LECTURERS AND TOPICS
Vittorio Somà, Université Paris-Saclay and CEA
Self-consistent Green’s function methods
Alexander Tichai, Technische Universität Darmstadt
Many-body perturbation theory
Andreas Ekström, Chalmers University of Technology
Bayesian inference and modelling of nuclear forces
Filippo Vicentini, Ecole Polytechnique and EPFL
Machine learning and neural network quantum states
Alessandro Lovato, Argonne National Laboratory
Machine Learning and Monte Carlo methods in Nuclear Physics
Kyle Wendt, Lawrence Livermore National Laboratory
Optimal control for quantum simulations
