Course catalogue
Create your own master’s programme by choosing between the different specializations of our partner universities.
Academic Programme
Implemented from September 2025
Introduction to Data Sciences (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)
Hours: (Lecture / Tutorial / Practical courses)
Lectures (10 h)
- Introduction to Data Science, data collection, processing, analysis and archiving
- API (Application Programming Interface) – a powerful data mining tool
- Reproducibility of published experimental results, Open Science and FAIR
- Big Data - brief overview of issues and challenges
Practical laboratory exercises (12 h)
- Hands-on use of databases and experimental data repositories
- Examples of shared data applications in chemistry and structural biology
- Practical use of the programmatic API implemented in various databases and web-servers
- Applications of big data in practice, machine learning in chemistry and structural biology
- Reprocessing and re-use of raw experimental data
The covered topics include the characteristics and operations associated with the creation, gathering, and use of research data. The following issues will be presented:
- How to handle raw data obtained from experiments (e.g. synchrotron X-ray diffraction, NMR spectroscopy, cryo-electron microscopy)?
- Required procedures to get a working dataset, the first stage of interpretation
- Overview of methods for analyzing experimental data
- Storing, retrieving and sharing data
- Reproducibility, validation and re-using data (Open Science and FAIR initiatives)
Students will learn in practice the methods and techniques that are necessary to exploit vast amounts of research data and to extract information from large heterogeneous datasets or use them in machine learning protocols. During hands-on laboratory exercises, students will be introduced to selected real scientific problems. They will prepare dedicated software queries to mine data from repositories and databases, learn how to process and archive experimental data, and how to improve search efficiency and precision.
Pre-requisites:
- Familiarity with the basic math and statistic concepts.
- Curiosity about research and playing with data.
Hours Lectures 12 h Laboratory 9 h
Teaching Staff: Miroslaw Gilski
Hours: 22 hours
Grading system in % (homework, oral presentation, lab training, final exam)
Recommended books & articles
- Introduction to Data Science, Jeffrey S. Saltz and Jeffrey M. Stanton, Sage Publ. (2017).
- An Introduction to Data, Everything You Need to Know About AI, Big Data and Data Science. Francesco Corea, Springer Nature, (2019).
- A public database of macromolecular diffraction experiments, M. Grabowski, K.M. Langner, M. Cymborowski, P.J. Porebski, P. Sroka, H. Zheng, D.R. Cooper, M.D. Zimmerman, M. Elsliger, S.K. Burleyd and W. Minor, Acta Cryst. D72, 1181-1193, (2016).
- Data sharing in structural biology: Advances and challenges, M. Grabowski et al., in Data Sharing: Recent Progress and Remaining Challenges-Computer Science, Technology and Applications (Nova Science Publishers), pp. 29–68, (2019).