General:

• We are going to prepare you for your internship by teaching you essential skills & knowledge
• Community building among students.
• Getting to know the group leaders and their research.
• Become computational: Learn to write scripts in Unix, make pipelines using bioinformatic methods, and learn about version control.
• Become familiar with common bioinformatic and biocomplexity methods (algorithms).
• Learn to interpret data by modeling
• Become a quantitative biologist

Learning goals

general

·         Can read and understand primary literature and critically evaluate their own understanding

·         Can understand and adapt an existing workflow to perform a systematic and reproducible bioinformatics analysis

·         Understands the FAIR principles and the issues related to handling, storage and sharing of data and code

bioinformatics

·         Understand, explain and apply the basics of dimension reduction, clustering (linkage and distances) and visualisation of high dimensional data

·         Can understand and explain the concept of supervised/unsupervised machine learning and their limitations

·         Understand and explain the concepts of cross-validation and overtraining

·         Understands and explain the basics of Hidden Markov Models and its fields of application

biocomplexity

·         Understand and explain the parameter curse and ways to avoid excessive parameter numbers

·         Understand, explain and apply model parameter fitting

·         Understand and explain the relevance of space, stochasticity and multiple scales for model behaviour

·         Understand and explain basic concepts in network theory and motifs

·         Understand and explain basic concepts of excitable media and their applicability to a broad range of biological phenomena

·         Understand the basics of numerical integration