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The course is an introduction to the interdisciplinary field of Environmental Humanities, providing insight into this dynamic and emerging area and demonstrating how to integrate humanities subjects and perspectives into other domains of…

This multi-disciplinary course treats the question of life in the Universe. Where can life have developed? Must it be on a planet similar to Earth? How does life on a planet develop and evolve? Under what extreme circumstances can…

In this course you will learn more about the structure and evolution of stars. The course contains amongst others: an overview of the different phases of the evolution of a star, the magnitude system and using it for stellar astronomy, the…

The course contains the following parts: Newtonian gravitation and dynamics. Reference systems and units. Galactic coordinates. Astrometry and the determination of the distance, the motion and distribution. The HR…

The course contains the following parts: Basic probability theory and statistics. The concept of probability, probability distributions and Bayes' theorem. Sampling, moments, correlation, and order statistics. Graphical presentation of…

The course treats: • Differential equation based modelling: Numerical methods to solve ODEs and convergence order. Applications to for instance predicting the spread of diseases and cell programming will be used. • MCMC based modelling…

BERN02. The overall learning outcome for the course is to let the students work with and combine two fields for data analysis in computational science: reproducible work flows and statistical learning. This includes to be able to…

Mathematical modelling is an essential tool within earth system sciences to understand processes and test hypotheses. For example, hypotheses related to ongoing climate changes are tested with models, and the estimated impacts of future…

Recent development in machine learning have led to a surge of interest in artificial neural networks (ANN). New efficient algorithms and increasingly powerful hardware has made it possible to create very complex and high-performing ANNs…

In this course you will learn how mathematical and computational physics methods can be applied to biological problems. Topics include deterministic and stochastic simulations of biochemical systems, population models, spatial models and…

The course treats: Important concepts in uncertainty quantification (UQ), such as verification vs. validation, intrusive vs. non-intrusive, and forward vs. inverse UQ Numerical discretisation for PDEs with random parameters: stochastic…

This course is intended to present some aspects of biology from a physics perspective. Among the topics covered are: cell structure, random walks and diffusion, entropic forces, chemical forces, macromolecules, and molecular mechanisms and…

Nature is complex and often several factors interact also in seemingly simple processes. Models are useful tools to disentangle complicated relationships, to build a comprehensive description of a process, or simply to present your own…