Introduction to GLP, GMP and quality control
25 March 2026 | 9:00 – 17:00 | in-person
This course will give a comprehensive overview on the concept of quality assurance in the framework of the pharmaceutical industry, including fundamental aspects and tools applied in practice, with an emphasis on GMP. Modern approaches of risk assessment will be addressed just as the implementation of CAPAs (corrective and preventive actions), dealing with OOS (out of pecification) results and realizing change control in a fixed process.
Also data integrity is an important issue of the course. Practical units at the end of the course will give participants the chance to practically implement the knowledge acquired on the example of case studies.
In details the course will deal with the:
- need for and origin of quality assurance
- concept and definition of quality
- regulatory key players and requirements
- applied quality standards on the example of GMP
- main pillars of a quality assurance system
- modern tools in quality assurance (risk analysis, CAPA, change control, OOS….)
Workshop: Gelingende Kommunikation in Projekten
November 3rd, 2025 | in-person (Campus Westend)
Inhalt: Ob in Forschungsprojekten an der Universität oder in Projekten außerhalb der Wissenschaft – erfolgreiche Zusammenarbeit hängt nicht nur von fachlichem Können ab, sondern in hohem Maß von gelingender Kommunikation. In Projekten treffen oft unterschiedliche Interessen, Arbeitsstile und Hierarchien aufeinander. Wer Projekte leitet oder daran mitarbeitet, muss unterschiedliche Interessen abstimmen, Informationen klar vermitteln und auch in angespannten Situationen konstruktiv im Gespräch bleiben. Neben fachlicher Exzellenz entscheidet vor allem eine gelingende Kommunikation darüber, ob Projekte reibungslos verlaufen oder Konflikte entstehen.
In diesem Training lernen die Teilnehmenden, wie sie sowohl in akademischen als auch in außeruniversitären Projekten klar, wertschätzend und zielorientiert kommunizieren. Sie erfahren, wie man Missverständnisse früh erkennt und vermeiden kann, Gespräche strukturiert führt und bei unterschiedlichen Positionen gemeinsame Lösungen findet.
Schwerpunkte:
- Besonderheiten der Kommunikation in Projekten
- Umgang mit unterschiedlichen Interessen, Prioritäten und Rollen
- Deeskalierende Gesprächsstrategien in angespannten Situationen
- Strukturiertes Vorgehen bei der Lösungsfindung
Methoden:
Das Seminar bietet kompakte theoretische Grundlagen, praxisnahe Fallbeispiele aus dem Forschungsumfeld und Möglichkeiten, das Gelernte direkt auszuprobieren. Die Teilnehmenden entwickeln individuelle Strategien, um ihre Kommunikation im Projektumfeld nachhaltig zu verbessern. Dafür werden u.a. folgende didaktische Methoden eingesetzt:
- Gruppen- und Kleingruppenarbeit
- Reflexionsübungen zum eigenen Verhalten und Handeln
- Arbeit an Fallbeispielen - Kommunikationsübungen
- Kurz-Inputs
Lake Como School of Advanced Studies: Frontiers in Inflammation Research – Mechanisms, Mediators and Biomarkers in the Onset and Resolution of Inflammation
29 September - 3 October, 2025
More information to follow!
Workshop: Introduction to Data Science with Python for Beginners
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
Workshop: Introduction to Data Science with R and Tidyverse
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
International Course on "Cancer and Cardiovascular Diseases: Common Mechanisms and Strategies of Prevention"
27.-30. July 2025
The GRADE Center BioMed FIRST together with the GRK 2336 AVE and the International School of Pharmacology Giampaolo Velo offers the course "Cancer and cardiovascular diseases: common mechanisms and strategies of prevention" (Course directors: Vincenzo Bronte, Bernhard Brüne, Pietro Minuz, Paola Patrignani, Dieter Steinhilber) which is co-funded by the ETTORE MAJORANA FOUNDATION AND CENTRE FOR SCIENTIFIC CULTURE.
Aim of the course is to focus on recent scientific knowledge about common mechanistic, pathophysiological, clinical and therapeutic aspects in cancer and cardiovascular disease. High-quality speakers will guide young researchers in these advanced topics by presenting their recent research results. In the tradition of the School, young course participants are encouraged to actively participate in the discussion and present the results of their scientific work as posters or short oral communications.
The course will be held from July 27-30, 2025 in Erice, Italy. The registration fee for the PhD students will be 450 €, which covers accommodation and meals, travel costs have to be covered by the participants.
Please contact Dr. Brigitte Held (held@grade.uni-frankfurt.de) until May 16h, 2025 for more information, if you wish to participate.
Projektmanagement
18. Juli 2025 | 9:00 – 17:00 | in Präsenz (Campus Westend)
Beschreibung folgt.
Storytelling for Scientists
postponed, t.b.a. | 9:00 – 17:00 | in-person (Campus Westend)
Scientists (like many people) like stories. Just because scientific research and concepts are rooted in data and experimental results, doesn’t mean they lack engaging stories.
In this workshop we’ll discuss the elements of a good story, explore examples of science storytelling and discuss how to use these ideas to not only entertain but also inform.
Participants will be asked to look at their own research and/or previous outreach efforts and in order to find the interesting narratives, characters and stories within.
Interested to find out how you can present your data, your results as a story?
Drug Discovery: From Target Validation to Lead Optimization with Dr. Aimo Kannt (Fraunhofer Institute for Translational Medicine and Pharmacology ITMP)
May 12th, 2025 | 16:00 – 17:30 | online
Probably more than in any other industry, sustainable growth and profitability of pharma companies rely on their ability to innovate and discover new treatments for diseases with unmet medical needs. This lecture will give an overview of the drug discovery process from target idea to preclinical development. It will focus on risk management in drug discovery and will outline scientific as well as organizational and strategic challenges associated with the different stages of pharma R&D.
Research Data Management in the Life Sciences
5.5.2025 | 9:00 – 11:00 | online | with Dr. Beate La Sala
In the life sciences, research data is important at all stages of the research process, from grant preparation, data accumulation, to analysis, publication, and even post-academic employment in industry. But how does one go about guaranteeing the effectiveness of your data?
This is a hands-on, online workshop that offers a practical and interdisciplinary introduction to research data management (RDM) with emphasis on the particularities of the life sciences. The course will outline key principles, essential tools and best practice for ordering, storing, analysing and sharing research data in a sustainable and repeatable manner. The workshop will combine conceptual insights with hands-on guidance, and will provide you with practical techniques that you will be able to implement directly in your research. It includes interactive elements and opportunities for discussion to ensure a clear understanding of best practices. Whether you are new to RDM or want to improve your current approach, this workshop provides the necessary knowledge you need.
No prior knowledge required.
Deutsche Version
