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AI4Science 2025 : 2nd Summer School on Advancing Scientific Discovery with AI

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Link: https://ai4science.sciencesconf.org/
 
When Jun 29, 2025 - Jul 4, 2025
Where Caen
Submission Deadline Apr 20, 2025
Categories    AI   generative model   machine learning   deep learning
 

Call For Papers

The 2nd Summer School on Advancing Scientific Discovery with AI is a comprehensive program designed to explore the intersection of artificial intelligence (AI) and scientific research.

This event aims to provide participants with a deep understanding of how AI techniques can be leveraged to accelerate and enhance the scientific discovery process across various domains.

The summer school will cover a wide range of topics, including machine learning for scientific data analysis, AI-driven experimental design and optimization, the use of generative models in scientific applications, and the integration of AI with physical simulations and theoretical frameworks.

Participants will have the opportunity to engage in hands-on workshops, case studies, and discussions led by renowned experts in the field, fostering a collaborative environment for knowledge sharing and interdisciplinary collaboration.

The overarching goal of the summer school is to equip researchers, scientists, and students with the necessary skills and insights to harness the power of AI in their own scientific endeavors.

By bridging the gap between AI and scientific discovery, this event aims to catalyze groundbreaking advancements and accelerate the pace of scientific progress.

The programme is designed for: PhD students, post-doctoral students and researchers in science.

Please note that this is a technical and scientific programme focusing on deep learning, not simply on how to use Large Language Models (LLMs). It is therefore primarily aimed at researchers who are familiar with mathematical modelling, numerical computation and Python programming.

A significant part of the programme consists of hands-on sessions where participants will use Python libraries (e.g. NumPy, PyTorch) to manipulate data and implement models. It is therefore recommended that you already have some experience with these tools. For the practical sessions, participants will need to bring their own personal computer.

However, if you do not have the necessary prerequisites, we can provide pointers to teaching resources to help you get up to speed. Our aim is to ensure that all participants are equipped with the necessary knowledge and skills to fully engage with and benefit from the programme.

If you have any questions or concerns about the technical requirements of the programme, please don't hesitate to contact us. We're here to support you and help you make the most of this learning opportunity

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