A course dedicated to emerging data-driven approaches in geosciences

Today, geosciences generate ever-increasing volumes of data from field measurements, sensors, imagery, numerical models and geographic information systems. Making effective use of these data has become a key challenge for understanding natural processes, managing resources and supporting decision-making.

In this context, Data Science and Artificial Intelligence methods are playing an increasingly important role. However, their implementation requires a sound understanding of data, models and their limitations.

This course has been designed to enable participants to become informed contributors to projects involving data and AI. By the end of the programme, participants will be able to understand data-processing workflows, critically interpret results and interact effectively with data scientists and modelling specialists.

Practical skills for professional and research applications

Upon completion of the course, participants will be able to:

• understand the main stages of a data-processing workflow, from acquisition to modelling;
• identify the most appropriate methods for a given geoscientific problem;
• assess the performance and limitations of machine learning models;
• critically interpret results produced by artificial intelligence methods;
• communicate effectively with data scientists, modellers and data engineers.

The objective is not to train AI specialists in a few days, but rather to provide participants with the foundations needed to integrate these tools effectively into professional and research contexts.

A progressive programme combining theory and practical applications

Delivered over five days and comprising 27 hours of instruction, the course combines lectures, hands-on workshops and feedback from professionals working in geoscience-related sectors.

Topics covered include:

• fundamentals of the data ecosystem;
• exploratory data analysis and data visualisation;
• databases and data management;
• data cleaning and preparation;
• principles of machine learning;
• critical evaluation of models;
• fundamentals of deep learning;
• digital twins and the integration of physical knowledge into AI models.

Guest speakers from industry and research will illustrate real-world applications of these methods in fields such as hydrology, water resource management, the oil and gas sector, and environmental modelling.

Teaching team

The course is coordinated by Anne-Laure Argentin and Alexandre Pryet, lecturers and researchers at ENSEGID - Bordeaux INP and members of the EPOC Laboratory (CNRS UMR 5805). Their expertise covers modelling, data science and their applications to geosciences.

They will be joined by several industry professionals from data-intensive sectors, providing participants with practical insights into current challenges and professional practices.

Practical information

• Dates: 4 - 8 January 2027

• Location: On-site at ENSEGID, Pessac (France)

• Format: In-person - 27 hours of training

• Language: English (course materials in English)

• Number of participants: 15–20

• Application deadline: 30 October 2026

Fees

• PhD students (all institutions) and self-funded participants: €450
• Professionals funded by their organisation: €700

Registration fees include course materials, access to the datasets used during the training sessions, and a certificate of attendance.

Application requirements

Applicants are invited to submit:

• Full name and affiliated institution/organisation;
• Status: PhD student, professional, or other;
• Research topic or area of expertise;
• Python proficiency level: beginner, intermediate, or advanced;
• Curriculum vitae (CV);
• Cover letter outlining your current research or professional context, your motivation for attending the course, and the objectives you hope to achieve through participation.