The QLS Diploma programme is aimed at providing students with a quantitative and theoretical background that can allow them to access postgraduate programmes in a broad range of disciplines, including biophysics, quantitative biology and neuroscience, theoretical systems ecology, economics, data science, and machine learning.


The diploma is organized along five main courses, on:

  • Probability and Information Theory
  • Quantitative Biology
  • Neuroscience
  • Ecology and Evolution
  • Machine Learning and Artificial Intelligence

Each are aimed at giving a core theoretical background for analyzing and modeling different phenomena in life sciences. These are complemented with shorter topical and advanced courses, seminars and journal clubs.

The course descriptions are listed below. In addition to these, students will have to follow the "Spring College in Complex Systems" and take exams there.


Probability Theory

The first part of the course deals with classical probability and it aims primarily at acquiring a solid understanding of how to turn a problem expressed in common language, into a probabilistic model (e.g. urn models, balls and boxes, random walks, branching processes, etc) and how to go from that to a quantitative answer (with combinatorial arguments, generating functions, etc). The second part deals with sequences of many random variables and the asymptotic behaviour in probability. We discuss typical behaviour (law of large numbers, limit theorems) and atypical behaviour (large deviation theory). This part also discusses probability from the point of view of information theory. Statistical mechanics and statistical learning are also discussed from this perspective.