PE&RC Postgraduate courses

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Mixed Linear Models
29-30 June 2017
In this module we discuss how to analyse data for which the assumption of independence is violated. So: Do you have a nested experimental set-up? Or repeated measurements? Or weight of the same animal over time? Or pseudo-replication? Then, you are likely to need Mixed Models. In this course, you will learn all about it!
Animal Movement Analysis
2 - 7 July 2017
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 and analysis of movement data. The course combines several guest lectures from international experts in the field of animal movement research.
Environmental Signaling in Plants
28 - 30 August 2017 (Note that all PE&RC PhDs who are entitled to a reduced fee will receive the reduced fee alike EPS PhD candidates)
Our society depends on plants, not only as the primary source of food and dominating role in ecosystems, but also as provider of commodities such as pharmaceuticals and building materials. In many ways plants are essential for human well-being. This course has an attractive program with many expert speakers in the field of environmental signaling in plants.
Human-induced land degradation
Saturday 2 - Sunday 10 September 2017
This course uses an interdisciplinary approach to learn about the different syndromes of soil degradation in different countries of the world and their consequences for people and the environment. Furthermore, solutions and sustainable management strategies are discussed.
R and Big Data
5-6 October 2017
The main aims of this course are to introduce participants to Big Data and the similarities and differences between regular modeling approaches and big data modeling, to help them understand the possibilities and limitations of R in big data research, to introduce them to high performance computing and to reproducible research. This course is aimed at experienced R users and should not be seen as a course to learn R.
Multivariate Analysis
16, 17, 18, 20 and 24 October 2017
The course is mainly based on the book "Multivariate Analysis of Ecological Data Using CANOCO 5" by Petr Smilauer and Jan Leps (2014). Practical exercises, the use of Canoco for Windows (4.5) and GenStat for Windows and interpretation of the output are important elements of the course.
Intro to R for statistics
23 - 24 October 2017
The aim of this course is to provide an introduction to R, a language and environment for statistical computing and graphics. Focus of the course will be on getting familiar with the R environment, to use R for manipulation and exploration of data, and to perform simple statistical analyses. Hands-on exercises will form a large part of the workshop.
Bayesian Statistics
25 - 26 October 2017
Nowadays, with the advance of computing and Markov Chain Monte Carlo (MCMC) algorithms, Bayesian statistics is becoming a powerful alternative for traditional Frequentistic statistics. Participants will be surprised how easy they can tackle problems that are quite complicated to handle with traditional Frequentistic statistics.
Linking Community and Ecosystem Dynamics
Sunday 12 - Friday 17 November 2017
This course focuses on theoretical concepts and how these can be used to link communities to ecosystems in order to understand how environmental change affects community and ecosystem dynamics. We will develop conceptual ideas, using examples from many terrestrial, marine and aquatic ecosystems, like savannas, deserts, forests, soils, streams, oceans, etc..
Uncertainty Analysis of Dynamic Models
11 - 15 December 2017
The purpose of this course is to make the participants familiar with general statistical concepts describing uncertainty, and methods to compute prediction uncertainty coming from uncertain parameter values. We introduce dynamic input-state-output systems and methods to write your model in this format.
Geostatistics
Monday 5 - Friday 9 February 2018
Geostatistics is concerned with the analysis and modelling of spatial variability. It also addresses how quantified spatial variability can be used in optimal spatial interpolation and spatial stochastic simulation. Fields of application include hydrology, soil science, ecology, geology, agriculture, and forestry.