Study of mathematical methods and algorithms in bioinformatics: Topics: Probability and statistics (prerequisites, statistical estimation, Markov chains, evolutionary models, sequence alignment), Hidden Markov models (Viterbi algorithm), Bayesian networks (principles, network inference), sequence alignment and phylogenetic trees (evolutionary relations, multiple sequence alignment, tree building).

Study of computational techniques, algorithms and data structures used to solve problems in computational biology. Topics: basic biology, string alignment, phylogeny (distance, character, parsimony), molecular evolution, multiple sequence alignment, probabilistic and statistical models, Markov models, microarrays, dynamic programming, maximum likelihood and specialized DNA and protein analysis.

Students learn how to effectively retrieve, critically judge, analyze, and manage scientific information – important skill sets in chemistry and life sciences where scientists need to deal with vast amounts of information. The course, using practical examples, also covers scientific writing, visualizations, science communication and state-of-the-art technologies such as AI, text and data mining.

This introductory class provides an overview of the basic scientific writing techniques and a guideline to presenting scientific data, together with guided exercises and hands-on training. It is devised to accompany the research projects within the curriculum of the MSc in Pharmaceutical Sciences.

This 1-semester course (14 x 1 hour) introduces students to the practical and theoretical principles of scientific writing in English. To improve their language skills, students will do practical exercises and write short scientific texts, which will be returned with feedback from the instructor.