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University of Michigan, Leinweber Center for Theoretical Physics

The 3rd Michigan Cosmology Summer School will take place in Ann Arbor, MI on June 2-6, 2025. The goal of the school is to help educate a generation of young scientists on the cutting edge developments in cosmology. Lectures will cover a broad range of topics, focusing on the interface of data and theory, and including computation, statistics, and machine-learning techniques. The format will be hybrid, with participants expected to attend in person but open to online participation as well. Outdoor activities will be organized. Registration will open in Spring 2025.

Tél.: [7346153289];     Email.: huterer@umich.edu

cosmology, summer school

The Department of Mathematics and Statistics at the University of Massachusetts Amherst will host its fourth annual summer school on Physical Mathematics of Quantum Field Theory during the week of June 9 - 13, 2025. The aim of the workshop is to introduce young researchers on the boundary between mathematics and physics to recent ideas in classical and quantum field theory using notions from complex geometry and algebraic topology. This year there is a focus on 4d gauge theories and their relationship with representation theory, algebraic geometry, and topology. The target audience of the workshop is advanced graduate students and post-docs, from both mathematics and theoretical physics. The schedule will be designed to include a large amount of time for participants to interact. We hope especially to encourage conversations between mathematicians and physicists.

Physique des hautes énergies, des particules et des champs,    Physique mathématique

Radiochimie et chimie nucléaire,    Cours et événements pour étudiants en chimie

Radiochimie et chimie nucléaire,    Cours et événements pour étudiants en chimie

Simons Laufer Mathematical Sciences Institute (SLMath)

The summer school aims to expose participants to formal methods that can facilitate principled scientific discovery. The school will cover some of the basic automated statistical inference (in the form of machine learning techniques) and reasoning methods that are commonly used in scientific discovery, as well as novel techniques developed to tackle open questions and issues. This summer school will address novel computational methods for scientific discovery and focus on fusing axiomatic knowledge and experimental data to enable principled derivations of models of natural phenomena along with certificates of the consistency of these models with background knowledge specified as axioms.

Cours et évènements pour étudiants en mathématiques,    Physique mathématique