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
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