Dates: 17, 18, 19 November 2026.
Format: Online, 2pm - 4pm CET, 2 hours per day (6 hours total).
Zoom link: https://zoom.us/meeting/register/EFrxGZmwQqCmMwllsBGUDg
Target audience: Physics undergraduate and postgraduate students worldwide
Speakers:

• Kate Shaw, Associate Professor at the University of Sussex.
• Johanna Senk, University of Sussex, UK, & Research Centre Juelich, Germany, works on computational models to study the structure, dynamics, and function of neuronal networks.
• Tom Stevenson, Senior Research Fellow at the University of Sussex, works on the ATLAS experiment at CERN.
• Muhammad Alhroob, Research Assistant at the University of Warwick, works on the ATLAS experiment at CERN.

Overview:
This introductory ICTP GLOBAL - PWF course will demystify artificial intelligence and explore what AI actually is, how it works, and how it can be useful in physics.
The course will introduce the main ideas behind machine learning, neural networks, natural language processing and modern large language models, including attention mechanisms.
Participants will also explore AI-assisted scientific programming, agentic coding workflows, and applications to physics research. The course emphasises scientific validation, critical thinking, limitations, and responsible use of AI.
By the end of the course, participants should be able to:

• Explain basic concepts underlying AI, machine learning and large language models.
• Recognise commonly used machine learning approaches in physics, including decision trees and neural networks.
• Understand examples of how machine learning is used in particle physics.
• Describe how LLMs work, what they can and cannot do, and how they can support scientific programming
• Understand the importance of validating AI-generated results in scientific research.
• Identify opportunities to begin using AI in their own physics studies or research.

Prerequisites: No previous experience of AI or machine learning. Basic Python knowledge is recommended for practical examples.

PROGRAMME:
Tuesday 17 November, 2pm CET: Introduction to Artificial Intelligence
Speaker: Johanna Senk

• What is artificial intelligence?
• AI, machine learning and deep learning: key distinctions.
• How machine-learning algorithms learn from data.
• Supervised and unsupervised learning.
• Examples of random forests, boosted decision trees and neural networks.
• Applications of AI across the physical sciences.

 
Wednesday 18 November, 2pm CET: Machine Learning and Neural Networks in Particle Physics
Speaker: Tom Stevenson

• Applications of machine learning to particle physics.
• Boosted Decision Trees and Neural-network fundamentals.
• Training, testing, overfitting and validation.
• Particle classification and experimental data analysis.
• Examples from particle physics experiments.
• Brief introduction to more advanced architectures, including graph neural networks.

 
Thursday 19 November, 2pm CET: Large Language Models and AI for Scientific Programming
Speaker: Muhammad Alhroob

• Natural language processing and large language models.
• Tokens, transformers and attention mechanisms.
• How LLMs generate text and code.
• AI-assisted programming using Python.
• Introduction to agentic coding workflows.
• Opportunities, limitations and risks for scientific research.
• Scientific verification of AI-generated code and results.