(Post-)Graduate Support at its Best!

30. März 2026 | 9:00–17:00 | Campus Westend

In Zeiten von alternativen Fakten und Fake News ist es wichtiger denn je, über Wissenschaft zu sprechen. Doch wie findet man den richtigen Draht zum Publikum und wer soll eigentlich angesprochen werden? In diesem Kurs erkunden wir, wie man die eigene Wissenschaft und das eigene Thema adressatengerecht aufarbeiten kann und welche Kanäle es für eine sinnvolle Kommunikation gibt.  

Themen:  

• Grundlagen und Akteure der Wissenschaftskommunikation
• Komplexes auf den Punkt bringen
• Welche Netzwerke für welchen Zweck?
• Adressatengerechte Ansprache
• Erste Tipps für eine eigene Social Media-Strategie
• Umgang mit Negativität (insbesondere auf Social Media-Plattformen)

Anmeldelink

23. Feb. 2026 | 09:00-13:00 | in person

This four-hour workshop aims to introduce participants to inter- and transdisciplinarity as modes of doing sustainability research. The workshop is designed to lower the entry threshold to engage with these modes of doing research and might be a very first starting point for further involvement.

Description:
We will begin with a general introduction to inter- and transdisciplinary research, covering its development, key figures and core concepts. We will look at research projects as case studies to gain a general understanding of potentials and challenges of these modes of research. 

Once the participants have gained a first understanding of both modes of research we will engage in a collective mapping exercise. This will help the participants to explore self-positionings in their current research and envision how they would like to design their future research relations. Depending on the participant's interests, the workshop will include a brief safari through the jungle of existing tool boxes for inter- and transdisciplinary research. 

Methodology:

• Presentation to provide basics on inter- and transdisciplinary research
• Interactive Mapping Exercise concerning participants current and aspired research relations
• Toolbox Safari exploring toolkits for inter- and transdisciplinary research
• Discussion rounds and experience exchange: Opportunities to address specific questions and challenges related to inter- and transdisciplinary research

Conditions:
Participants should be interested in both interdisciplinarity and transdisciplinarity. The former refers to research across disciplines, while the latter refers to research involving non-academic actors.

Target Group:
Researchers with little or no experience with inter- and transdisciplinarity from all backgrounds who are interested in a first contact to these modes of research. Participants should have (some) research experience.

Trainer: Dr. Britta Acksel (Wuppertal Institut für Klima, Umwelt, Energie gGmbH)

works as scientific advisor for methods of transformation research at the Wuppertal Institute. She is a cultural anthropologist interested in sustainability and holds a doctorate from Goethe University Frankfurt. Britta has vast experience in working in inter- and transdisciplinary contexts and research projects which she is enthusiastic to share

Please register here

29 October / 16 December | 10:00 | Online

16 September | 09:00 - 16:30 | Online

Joint course with GRADE Centers BRAIN and GRADE Center Sustain

Most academic fields require proficiency in at least one data-centered analysis tool. For many, the R programming language has become the tool of choice. However, the first steps in coding can be intimidating and discouraging --- primarily if you have never worked with a programming language like R before. This course provides a results-oriented, applied, and hands-on introduction to the most critical parts of a Data Science project in R. We will introduce the libraries and frameworks necessary for your analysis and focus on teaching you the implementation and application of those tools with small examples that you can work on yourself.

Our goal is to show you the scope of possibilities within R and leave you with the impression that you can confidently implement your empirical projects in R. We will focus on the Tidyverse ecosystem, a consistent and intuitive framework for building your data analysis from start to finish. After completing this course, you will know how to apply the essential Tidyverse tools for everyday Data Science tasks in R --- primarily data wrangling, data visualization, and communicating results.

Description:

This course aims at beginners who are completely new to R as a programming language and/or want to learn about the Tidyverse ecosystem. We structured the course in the following way:

You will learn:

• Reading data into tibbles with readr and a short primer on data types.
• Plotting with ggplot2: aesthetics, geoms, and the grammar of graphics.
• Data wrangling with dplyr: mutate(), select(), filter(), group_by(), summarize(), …_join(), and pipe-operator %>%.
• Communicating your analyses with RMarkdown in a reproducible way.
• Univariate and multivariate linear regression with lm()

This course will not cover deeper statistical or theoretical concepts as we focus on applied coding.

Methods:

• The course will alternate between short introductions to concepts or methods and small do-it-yourself coding exercises.
• In between the three sessions, you are encouraged to work on provided exercises that further deepen your understanding

Please register here

17 September | 09.00 - 16:30 | Online

Joint course with GRADE Centers BRAIN and GRADE Center Sustain

Data analysis plays a critical role in many academic disciplines, and the Python programming language has become one of the standard tools within the Data Science community. However, the first steps in coding can be intimidating and discouraging—especially if you have never worked with a programming language before. This course will introduce programming with Python and how to use it for data analysis.

After successfully completing this course, you will be able to understand the fundamentals of the Python programming language. This skill set includes basic data analysis by data wrangling, visualizing data, and applying simple statistical models in Python. Our goal is to show you the scope of possibilities within Python and leave you with the impression that you can confidently implement your own empirical projects in Python.

Description:
This course aims at beginners. Hence, we will cover Python's programming fundamentals, such as variables, loops, and logic statements, before we dive into Data Science. 

You will learn:

• Syntax and basics of Python and how to use Jupyter Notebooks as a coding environment
• Data analysis, data wrangling, and data visualization using numpy, pandas and matplotlib
• Introduction on how to use simple statistical models in Python with scikit-learn

This course will not cover deeper statistical or theoretical concepts as we focus on applied coding.

Methods:

• The course will alternate between short introductions to concepts or methods and small do-it-yourself coding exercises.
• In between the three sessions, you are encouraged to work on provided exercises that further deepen your understanding.

Target group: PhD students and Postdocs with no or minor prior experience in programming

Please register here

!!! POSTPONED !!!

In der globalisierten Hochschullandschaft orientieren sich viele Universitäten weltweit an westlichen Normen, unter anderem westlichen Standards zu Good Governance und guter wissenschaftlicher Praxis. Dabei prallen diese Normen außerhalb der privilegierten Länder und Institutionen des „Globalen Nordens“ häufig auf Gesellschaften mit völlig anderen, zum Teil konträren Prägungen und Normen. Was bedeutet es, in einem solchen Umfeld als westlich geprägte(r) Wissenschaftler*in zu arbeiten? Wie gelingt es, mit tradierten Normen konstruktiv umzugehen und Brücken zu bauen? Was bedeutet es, eine multiethnische Arbeitsgruppe zu führen? Wo liegen Grenzen, deren - gut gemeinte - Überschreitung Beteiligte möglicherweise überfordert? Ist es überhaupt ratsam, in einer an Performance Metrics orientierten, neoliberalen Wissenschaftslandschaft in ein solch schwieriges Umfeld zu wechseln?

Der Workshop basiert auf Erfahrungen im Südpazifik und andern Ländern des „Globalen Südens“. Auf dieser Grundlage werden u. a. anhand ausgewählter, erlebter Szenen Handlungsmöglichkeiten besprochen und diskutiert. Zugleich bietet er Raum für Gespräche über Wege der Entscheidungsfindung zur Frage „Soll ich im Entwicklungsforschungs-Kontext arbeiten - oder lieber nicht?“

Referent:  Prof. Dr. Hans Juergen Boehmer
Leibniz Universität Hannover
Nienburger Straße 17
- Editor, Endangered Species Research (ESR)
- IUFRO Taskforce 'Monitoring Global Tree Mortality Patterns and Trends'
- IUFRO Working Group 4.02.01 'Resource data for the tropics'

Link zur Anmeldung