WASCAL PhD Programme on Climate Change and Human Habitat released the new call for application

WASCAL PhD Programme on Climate Change and Human Habitat released the new call for application

April 21, 2021

On the 21st of April, our Nigerian partner at the Federal University of Minna (Niger State, Nigeria) released the new call for application for the Doctoral Research Programme on Climate Change and Human Habitat. We at the Department of Remote Sensing at the University of Würzburg are absolutely delighted about this new achievement. We will join this programme through lecturing and supervision and in our function as a member of the WASCAL international advisory board.

The application is now open for all qualified candidates from the 11 West African partner countries of WASCAL: Benin, Cabo Verde, Burkina Faso, Côte d’Ivoire, Ghana, Mali, Niger, Nigeria, Senegal, The Gambia, and Togo. Of course, self-financing students can also apply from countries other than WASCAL countries. The deadline for application will end on the 30th of June 2021.

The ongoing support and funding of the programme from the German Federal Ministry of Education and Research (BMBF) are highly appreciated.

We are very much looking forward to the applications of many highly qualified candidates!

you may also like:

Successful MSc defense by Sonja Maas

Successful MSc defense by Sonja Maas

Big congratulations to Sonja Maas, who successfully defended her Master thesis today on the highly relevant and increasingly pressing topic: “Comparing LiDAR-Based Acquisition Strategies for Forest Management Planning in a Mature Beech Stand.” Supervised by Dr. Julian...

Visit at the Institute for Geoinformatics (IFGI) at University of Münster

Visit at the Institute for Geoinformatics (IFGI) at University of Münster

Two days ago, our PostDoc Dr. Jakob Schwalb-Willmann visited the Institute for Geoinformatics at University of Münster to give a talk at IFGI’s GI Forum titled “Can animals be used to classify land use? Employing movement-tracked animals as environmental informants using deep learning”.