Women As Research Peers for Information Technology

Women scientists at the SCC network

Translated with DeepL.com

Women As Research Peers for Information Technology aka warp4IT is an association and network for (young) female scientists at the SCC.

The fact is that the proportion of women in computer science and IT-related professions is low. We want to change this because we find our professions in computer science and data science research diverse and enriching. Digital processes and complex IT systems have become an integral part of our society. However, the underlying research and development is largely carried out from a male perspective. We are committed to equal opportunities and the equal participation of women in IT.

What we want:

  • Raise awareness of the expertise of female computer scientists in research and careers,
  • make young female scientists aware of their own potential to shape our society and
  • exchange ideas, give advice and support each other.


Networking, role models and practical insights into everyday working life are essential for this (® Women in Science & IT). warp4IT does exactly that: We offer concrete opportunities for networking and exchange as well as subject-specific information on a career in IT.

The warp4IT seminar series

In our seminar series, female researchers of all stages of life, age and career with the most diverse backgrounds present their careers in (computer science) research. Female scientists from the SCC, KIT, but also from external institutions, tell how their path has developed and how they got to where they are today - informally, authentically, honestly. A subsequent discussion round invites participants to share their experiences and offers a protected environment for discussing personal questions.

Our hybrid seminars are aimed at female students and researchers with an affinity for computer science at all stages of life, age and career. Are you interested? Then get in touch with our contact person Marie Weiel (see contact box) to be informed about upcoming seminar dates!

warp4IT-Mentoring for female students interested in IT

We offer a science-oriented mentoring program for female students interested in computer science at KIT. Through an employment as a Hiwi, female students get the opportunity to work on projects from current research in computer science and data-driven computing and get a real insight into the working life of scientific staff at the SCC.


Are you a student at KIT, interested in computer science and IT and wondering what comes after your studies? Do you have your own project idea on AI, scientific computing & co. that you would like to implement under supervision? Would you like to find out why your knowledge is particularly in demand in computer science and data science? Then apply as a Hiwi at warp4IT!

What we offer:

  • Support from experienced and committed female scientists at the SCC as your contact person for all aspects of your career as a research assistant and IT specialist
  • Contact with like-minded people and concrete opportunities for networking and exchange
  • Training and further education opportunities as well as subject-specific information on a career in IT

What you bring with you:

  • Enthusiasm for computer science and data science research
  • Commitment and motivation to work on your own project
  • Interest in interdisciplinary collaboration
  • Affinity for technical concepts and methods
  • Basic knowledge of programming is helpful
Applications at any time with your CV and a short letter of motivation to warp4it∂lists.kit.edu! German or English welcome!

Ich habe gelernt, dass es nicht nur den „einen Weg“ in die Wissenschaft gibt und der jeweilige Lebensweg sehr unterschiedlich sein kann. Ich bin sehr dankbar für all die Erfahrungen, die ich sammeln konnte und habe unglaublich viel gelernt, was mich in Zukunft weiterbringen wird und woran ich gut anknüpfen kann.

Marion M., Mentee bei warp4IT

Past warp4IT Projects

Dr. Charlotte Debus: How to make our power supply more reliable?

Artificial intelligence (AI) plays a crucial role in time series analysis and prediction. One example is controlling and optimizing electric power grids – an ever-important challenge in light of the upcoming energy revolution. Modern AI algorithms can predict the energy consumption, which helps to improve grid stability and secure the supply. However, applying AI for critical infrastructures presents us with the following question: To what extent can we trust the forecasts of our models? To tackle this challenge, researchers have developed methods for quantifying uncertainties in neural networks.

In this project, we want to apply such methods to AI models for time series forecasting to evaluate their “trustworthiness” by estimating their uncertainties and error rates. You will implement a “Sequence2Sequence” model in Python, train it on European electricity consumption data, and determine its prediction uncertainty as a reliability measure using the “Prediction Intervals” (PIs) and the “Lower Upper Bound Estimation” (LUBE) method.

Dr. Danah Tonne: How to read a pyramid like a book?

Egyptian pyramids are ancient evidence of a long­gone culture. Inside you will find a variety of hieroglyphs called “spells” that provide protection and good wishes for the afterlife of the deceased. Which spells were used for which context and how they are connected are questions of great interest to current researchers.

In this project, you will investigate the “International Image Interoperability Framework” (IIIF), a framework to standardize the handling of images and audio/video files on the web to, e.g., allow reuse of research data and tools. Currently mostly used for manuscripts and books, we want to explore potentials, pitfalls, and of course the applicability to pyramids to allow insights into this unique data treasure.

Dr. Eileen Kühn: How to enhance CNN-based learning with quantum computers?

In the noisy intermediate scale quantum (NISQ) era, devices have a limited number of qubits with little error correction, making developing useful computational algorithms for NISQ devices an urgent task. In this domain, quantum machine learning (QML) is gaining attention in hope of achieving computational speedup or better performance for some ML tasks. Especially parameterized quantum algorithms provide a possibility for QML towards quantum advantage in the NISQ era. In contrast to conventional quantum algorithms, they are inherently robust to noise and could benefit from quantum advantage by harnessing both the high dimensionality of quantum systems and the classical optimization scheme. 

In this project, you will design and analyze a hybrid quantum-classical algorithm by incorporating a parameterized quantum algorithm into a classical convolutional neural network (CNN) in Python. Our goal is not only to validate the classification performance on the well­known MNIST dataset in comparison to a classical CNN but also to analyze potential benefits of noise robustness.