Applications for the class of 2020-2022 are still open for self-financed students. Apply here

Deadline : June 15th, 2020 (midnight, Paris time).  Applications for scholarships are closed. 

Covid-19 updates: during the Covid-19 pandemic, the SERP+ master is taking measures to ensure that learning can continue with new teaching and assessment methods.

Master SERP+ Programme

Data Science and Applications to Chemistry


Computational tools: harmonizing competences (8 hours)

  • Basics of numerical analysis (2 hours)
  • Basics of Bayesian theory (3 hours)
  • Basics of regularization theory (3 hours)

Artificial Intelligence: the many aspects of data modeling (10 hrs)

  • Numerical Simulation (2 hours)
  • Inverse Problems (4 hours)
  • Machine Learning (4 hours)

Applications to chemical and biochemical data (6 hrs)

  • STM imaging (2 hrs)
  • Tracer kinetics (2 hrs)
  • Chemical Reaction Networks (2 hrs)


The general objective of the course is to provide students with a first overview of the main issues related to modern data science and its cultural background. The course has also two more specific objectives. The first one is to illustrate some computational tools representing the methodological basis for any artificial intelligence approach to data analysis problems. The second one is to describe three applications concerned with the use of data science methods in chemistry and biochemistry: the problem of the automatic recognition and classification of atomic species in Scanning Tunnelling Microscopy; the modelling of glucose metabolism by means of nuclear medicine data; the simulation of the chemical reaction network at the basis of a specific cellular transition in oncogenesis.


Students attending the course should know in advance the basics of

  • Linear Algebra (vectors, matrices and their norms; linear systems; inversion of a matrix; eigenvalues)
  • Calculus (properties of functions; limits and continuity; differentiation and integration)

Teaching Staff

Michele Piana, Dipartimento di Matematica, Università di Genova


24 (lectures)

Grading System

20% homework
80% oral presentation
mid-term exam: no
final-exam: yes