The aim of this course is to provide an introduction to R and R Studio. It introduces the participants to R language syntax, to enable them to write their own R code. They will also learn about R data-types and data-structures, and they will be taught how to explore the data and produce plots. The course will be a combination of lectures and practicals.

Extend participants' basic knowledge of R by teaching them more advanced programming concepts and the use of R for more complex problem solving, going beyond just statistics.

In this 5-day course, you will learn about the basics of computer vision, from the acquisition of good quality images to the use of Python programming to implement computer-vision solutions to extract relevant information for your domain. You will learn about more traditional image-processing techniques as well as state-of-the-art deep neural networks to process images and videos.

This course focuses on theoretical concepts, such as autocatalytic loops and positive and negative feedbacks between organisms in ecological networks as well as the importance of non-trophic interactions by ecosystem engineers.

This course introduces analog serious games (e.g. board and card games, narrative games) as tools to explore and foster food system transformation and challenges the participants to design new and/or adapt existing games and test them in a final event where they can showcase their prototypes. 

The aim of this course is to provide participants with skills to assist them in working with animal movement data including data management and organization, working with large tracking datasets, data exploration, visualization, annotating track data with environmental data and analysis of movement data.

Starting a PhD is an exciting step but it can also feel a bit overwhelming at times. Our First year Retreat is designed to help you get a better sense of what lies ahead and how to make the most of your PhD experience.

This retreat invites you to hit pause, look back, and explore how things are going  and how things can be improved!

Join us for an inspiring two-day retreat designed for PhD candidates in their final year!

Are you a researcher in ecology, environmental science, biodiversity science, or related fields? Are you encountering challenges in managing your research data? Join this course and learn about the things you can do now to make your data future-proof.

In the postgraduate course Chemical Ecology throughout the tree of life, 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.  

This seed systems course critically examines the performance and interplay of conservation frameworks, institutions, and stakeholders, with a focus on the opportunities and tensions inherent in integrated approaches and seed systems that support crop conservation.

Input data for spatial environmental models may have been measured in the field or laboratory, spatially interpolated, derived from remotely sensed imagery or obtained from expert elicitation. 

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 purpose of this course is to make the participants familiar with general statistical concepts describing uncertainty, and methods to compute prediction uncertainty and sensitivity coming from uncertain parameter values. 

This course is primarily aimed at participants who are in the start-up of a modelling project. We purposefully aim to involve people from different scientific backgrounds. The emphasis is not on the mathematical, computational, and statistical aspects of modelling per se, but on the elements of the modelling process before that. You will learn about the scoping of a model, and to think critically about the choices in modelling.

The course follows the geo-information cycle, guiding participants through data acquisition, storage, processing and visualisation.

This course will focus on the fundamental and applied aspects of the ecology and control of vector-borne diseases and their vectors.