Program

Frederic Jurie is a full Professor at the University of Caen Normandy with a background in computer science research. He holds a PhD in Machine Learning and Computer Vision, and has published in these fields. His career includes roles at CNRS and INRIA Rhône-Alpes, as well as directing the GREYC research laboratory (Normandy, France). His work focuses on Computer Vision and Machine Learning, particularly image and video interpretation. He has contributed to visual tracking, bags of visual words, metric learning, shape models, and image descriptors. He has also ventured into Deep Learning, developing new perception algorithms. Since 2019, F. Jurie has held a chair in Artificial Intelligence, now promoting the application of Machine Learning across various scientific disciplines, embracing the concept of "AI for science". frederic.jurie@unicaen.fr

Administrative coordinator at Unicaen

Clotilde NICOLLE /clotilde.nicolle@unicaen.fr
EU Project Manager- Carré international 

About the Summer School

This summer school provides a platform for researchers and students from diverse scientific backgrounds to delve into Artificial Intelligence and its applications in scientific research. Over five days, participants will explore current techniques, learn from experienced specialists, and collaborate with peers on relevant scientific problems. Each day focuses on a specific application domain and the related AI methodologies. This program aims at exploring the potential of AI to accelerate research and improve our understanding of complex scientific phenomena.

Daily Structure

Each day will be structured to facilitate learning and interaction, combining a variety of formats. Expect a blend of:

• Lectures: Comprehensive presentations introducing key concepts and theoretical frame- works.
• Hands-on Practical Sessions: Application of the newly learned AI techniques to con- crete scientific problems, in dedicated labs.
• Conferences: Seminars led by specialists in their respective fields.
• Exchange and Discussion Sessions: Engaging discussions to reinforce understanding and explore diverse perspectives, led by a scientific personality.
• Breaks and Free time: Time to recharge and connect with participants and speakers.

Sunday, June 29, 2025 (5:00 PM - 8:00 PM)

Welcome Reception: Participants can attend an evening of networking and introductions. Snacks will be served while participants share their motivations, backgrounds, and AI-related projects with fellow participants and speakers.

 Monday, June 30, 2025 (9:00 AM - 7:00 PM)

Theme: AI for Medical Applications

Focus: Medical Image Processing.

AI Foundations: Machine learning and deep learning basics, convolutional neural networks. Participants will explore how AI algorithms, specifically convolutional neural networks, are used in medical diagnostics and analysis from image processing.

Tuesday, July 1, 2025 (9:00 AM - 7:00 PM)

Theme: AI for Simulation Acceleration

Focus: Accelerating simulations in fields such as astronomy.

AI Foundations: Generative models.

Participants will learn about the potential of generative models in accelerating scientific simu- lations, exploring their application in high-performance computing.

Wednesday, July 2, 2025 (9:00 AM - 7:00 PM)

Theme: AI for Inverse Problems

Focus: Sensor localization in magnetometry and related fields.

AIFoundations: Physics-inspired neural networks.

Participants will discover the application of physics-inspired neural networks to solve inverse problems, using physical laws to constrain the neural architectures.

A part of the afternoon will be dedicated to a social event at the Caen Town Hall.

Thursday, July 3, 2025 (9:00 AM - 7:00 PM)

Theme: AI for Materials Science

Focus: Materials discovery and characterization.

AI Foundations: Uncertainty quantification, Bayesian models.

Participants will learn about the use of AI for materials discovery, combining Bayesian methods with deep learning to provide uncertainty in material predictions.

Friday, July 4, 2025 (9:00 AM - 5:00 PM)

Theme: AI for Particle Physics

Focus: Analysis of particle physics data and anomaly detection.

AI Foundations: Deep learning for physics data analysis, anomaly detection.

Participants will explore the application of deep learning techniques in particle physics, focusing on data analysis and anomaly detection in large datasets obtained from colliders and telescopes.