Courses open for registration

Resilience is the capacity of a system to maintain or recover certain functions while undergoing shocks and stresses. It emerges from the many interactions between people and natural and/or artificial system components, and the capacity of people to adapt. During this course, the participants become acquainted with different resilience concepts and their application from an interdisciplinary perspective. Accordingly, we will address how resilience theory can be used to tackle fundamental and societal issues from a socio-economic and bio-physical perspective and will provide a critical reflection on the relevance, use, and applicability of the concept of resilience. The objective of this course is thus to connect resilience concepts to viable applications by offering an efficacious analytical/computational approach.

Machine learning plays an increasingly important role in many scientific areas, including geo-information science and remote sensing, ecology, biosystems engineering, and bioinformatics. Today, scientific data are growing in complexity, size, and resolution, and scientists are challenged to leverage available data to inform decision making. In this course, you will learn how to model patterns and structures contained in data, and evaluate data-driven models, i.e. models that learn directly from observations the phenomena under study.

This on-site course will focus on these factors where a central focus will be on below-ground processes. Besides discussing the influence of the available resources on the success of intercropping, soil-root, root-root interactions will obtain much attention.

Classical statistics offers a powerful toolbox for data analysis. This toolbox, however, may not always be sufficiently flexible for modern data situations. The Bayesian framework allows for the integration and inclusion of information from many sources as well as a natural quantification of uncertainty in subsequent analysis.

This course will provide the participants with an overview of a range of methods related to the five soil functions and will provide detailed practical training in a subset of measures. The training will be a combination of lectures, laboratory and field sessions (interactive lectures and practical sessions each day). Assessing a range of measurement types, from simple visual assessments in the field, to training in microscope identification techniques for nematodes and earthworms, and functional measures in the lab such as MicroResp. All methods described in the course will be made available to participants as well as advice on how to analyse the data.

Modelling is a crucial part of today's science. Among other things, models are used for assessing sensitivity of systems to disturbances or changes in external factors, and for predictions of future system states. This course provides an introduction to modelling with a focus on systems analysis using dynamic simulation models.

Human induced land degradation is a very actual and increasing problem all over the world. Population growth, unsustainable management of natural resources as well as increasing amounts of harmful chemicals in the environment lead to severe land degradation. This phenomenon has to be considered in a socio-ecological context with respect to economic, cultural and social conditions. This course will be focused on an interdisciplinary approach to learn about the different syndromes of land degradation using Portugal as a case-study. Furthermore, we will look for solutions and sustainable management strategies.

While much of statistics focusses on associations between variables and making predictions, the aim of structural equation modelling is to establish causal relationships between variables. The focus will be on classical structural equation models with a small number of (latent) variables, but we will also give an introduction to recent developments on methodology for high-dimensional data.

This post-graduate course on Theoretical Ecology will focus on bifurcations in dynamical systems and participants will learn quantitative approaches to assess qualitative changes in biological systems that occur when conditions change.

In this workshop, participants will learn the principle of tidy data, how to transform and combine datasets using the tools from the tidyverse and how to generate advanced visualization with the ggplot2 package.

This course is for participants who want to deepen their knowledge of R programming and be able to use R to deal efficiently with computational problems and programming tasks. Participants will also gain more knowledge on working with R data structures and solving common problems, like working with and manipulating factors, extracting information from statistical models, working with other types of data (textual, time and dates) and handling multiple data files.

Climate change is a major threat to global food systems. Crop yields are expected to decline due to changes in temperature, water supply and CO2 levels. Moreover, as climate change models have predicted and what we already see today is that conditions will become much more erratic (sudden heatwaves, flooding, droughts etc) with dire consequences for plant growth. In this course, we will present and discuss the degree of crop-physiological responses to these changing conditions where the central issue to be addressed is the adaptive capacity of plant physiological traits to counter the changing environment.

In the postgraduate course Chemical Communications in the Anthropocene, we will focus on how man-made changes to the environment can influence chemical communications within and between microorganisms, plants, herbivores and disease vectors. We will not only focus on how chemical information can be collected and analyzed, but also on the environmental factors that can affect chemical communications and zoom in on the underlying mechanisms of producing and perceiving chemical information. There will also be two hands-on workshops on how to analyze large datasets in the field of chemical ecology.  

Microbial communities are vital to life on Earth.These invisible creatures catalyze global elemental cycles, regulate climate, and support human and environmental health and thereby play a critical role in mitigating climate change.

The aim of this course is to provide an understanding of the statistical principles underlying experimentation. A proper set-up of an experiment is of utmost importance to be able to draw statistically sound conclusions. The role of sample size, randomization and the reduction of unwanted noise factors will be highlighted. The way errors propagate will be discussed. The difference between experimental unit and measurement units and consequences for statistical analysis will be discussed.

Programming can serve multiple purposes. Purposes like developing applications and working with data are also very useful for research. For dealing with these issues, Python offers many libraries. Getting the skills of working with some of these libraries will enable future learning. This can be for more advanced programming applications, but also for self-learning to apply different libraries.