Course catalogue
Create your own master’s programme by choosing between the different specializations of our partner universities.
Academic Programme
Implemented from September 2025
Fundamentals in data science and machine learning (3 ECTS)
All courses during this semester
- Experimental methods on innovative research infrastructures - 5 ECTS
- Digital Micro-certification "The challenges of sustainable chemistry" - 10h
- Transferable skills : French language & interculturality (3 ECTS)
- Quantum mechanics towars quantum computing (5 ECTS)
- Winter school in Data Science (2 ECTS)
- Organic / Inorganic chemistry towards sustainability (5 ECTS)
- Kinetics and Electrochemistry (5 ECTS)
- Introduction to biophysics and microscopies for life science (5 ECTS)
All courses during this semester
- Luminescence spectroscopy of Lanthanides (3 ECTS)
- Summer School in Entrepreneurship (5 ECTS)
- Transferable skills: Polish course (3 ECTS)
- The molecules of life: from structure to chemical function (5 ECTS)
- Thermodynamics and soft matter (3 ECTS)
- Introduction to solid state (5 ECTS)
- Tech-infused perspectives on photochemical reaction dynamics (6 ECTS)
- Transferable skills: Portuguese course (3 ECTS)
- Summer School in Entrepreneurship (5 ECTS)
- Solid State Physics (5 ECTS)
- Molecular Energetics (3 ECTS)
- Laboratory of Materials and Surface Analysis (5 ECTS)
- Interfacial Electrochemistry (3 ECTS)
- Interfaces, Colloids and Self-Assembly (6 ECTS)
All courses during this semester
- 1-year research project - master thesis (equivalent 45 ECTS)
- Progress assessment of the research project (equivalent 6 ECTS)
- Weekly seminars (equivalent 4 ECTS)
- Special Topics in Chemistry (equivalent 5 ECTS)
- French language courses (3 ECTS)
- Nanosciences (6 ECTS)
- Medical applications of nanomaterials and radiations (6 ECTS)
- Top management, corporate law, and project writing for technology transfer and decision making (4 ECTS)
- Tracking ultrafast radiation-induced reactivity (3 ECTS)
- Applications for renewable energy and storage: solar fuels, batteries and hydrogen (6 ECTS) (6 ECTS)
- Scientific Writing and career objectives (2 ECTS)
- Surface Science and Nanostructuring at Surfaces (6 ECTS)
- Polymers for electronics and energy harvesting (5 ECTS)
- Electrochemical systems for fuel and electrolysis cells and batteries (6 ECTS)
- Project-based laboratory on device building (3 ECTS)
- Italian Courses (3 ECTS)
- Chemistry and Technology of Catalysis (5 ECTS)
Content
Multivariate analysis and Evolving processes
- Characterization, information, discrimination, prediction, Markov, …
Introduction to Machine Learning through supervised projects
- K-means, Kohonen neural networks, Generative algorithms, Machine learning, etc. Several topics (experimental and theoretical Chemistry, Molecular and Chemical Kinetics and Nanosciences) will be proposed to the students to apply the studied techniques.
Programming languages used in this course: Octave/Matlab and Python
Aims
Introduction
Artificial Intelligence and Machine Learning are revolutionizing research and discovery in many scientific disciplines including Material Science, Nanotechnology and Pharmaceutical Chemistry. High throughput methods in material and pharmaceutical research often generate huge datasets that require data mining and Machine Learning techniques to extract relevant information that is needed to make new discoveries. Computational Physics and Chemistry are increasingly experimenting with Big Data techniques to extend and accelerate their approaches to larger and more realistic systems and simulations. Furthermore, Machine Learning methods may potentially solve the so-called Inverse Design Problem, the prediction of the structure of a novel material (or pharmaceutical compound) from its desired properties.
What do we offer?
This program has been conceived as a combination of knowledge and skill-based courses. It will provide the student with the fundamentals of a valuable and universal tool which can be adapted to multiple types of problems and situations in Molecular and Nanosciences. After an initial introduction on the theoretical methods necessary to understand the fundamentals of Machine Learning (ML), the student will be able to either choose the supervised ML project of his interest or conduct a ML-based research proposed by the instructors. This formation will be complemented by Keynote Lectures given by ML experts from technological companies and academia.
Upon completion of the course, the students should be able to:
- Manipulate scientific data and extract the relevant information
- Validate/invalidate theoretical hypotheses
- Characterize the past and the future of evolving processes.
- Apply Machine Learning to Molecular Sciences and Nanotechnology problems
Teaching Staff
Van-Oanh Nguyen-Thi
Hours
25 hours