yDiv is the graduate school for young biodiversity researchers at iDiv. Its aim is to educate doctoral researchers in inter- and transdisciplinary biodiversity research.
yDiv offers first class training and support to doctoral researchers in a stimulating, international research environment. As part of iDiv, the graduate school offers unmatched opportunities of courses and networking. yDiv doctoral researchers accumulate valuable skills in assimilating knowledge and techniques from various disciplines, and combining different approaches in their work. In addition, we offer a wide range of transferable skills training.
Corona-related information for doctoral researchers
- Leipzig University (currently only in German): Informationen für Promovierende zum Umgang mit der Corona-Krise
- Friedrich Schiller University Jena: FAQ for doctoral candidates and postdocs about corona crisis
- Martin Luther University Halle-Wittenberg: Information on dealing with the novel corona virus (InGrA Blog), further posts
29-30 October 2020: Good Scientfic Practice (Online)
This course provides an introduction to the basic rules and values of a responsible conduct of research.
2-3 November 2020: Data Visualisation (Online)
This two-day workshop enables life scientists to effectively create figures based on quantitative data that add impact to their publications.
9-13 November 2020: yDiv Welcome Week
The yDiv Welcome Week introduces doctoral researchers to all the information and tools needed in order to successfully start their PhD career in integrative biodiversity science at iDiv. The week contains an introduction to the iDiv research and goals. Participants also get to know the yDiv offers and requirements, the doctorate examination regulations at the universities, learn tools to manage their PhD project and to communicate with their supervisors.
19+20 November 2020: Occupancy Modelling for Species Distribution Data (Online)
In this 2-day course, participants will learn the principles of occupancy modelling by simulation and analysis of real-world data. We will learn how to use standard R packages (“unmarked”) as well as more flexible (but more complex) models using JAGS.