Technology Driven Innovation (TDX)

Turn technological breakthroughs into Innovative solutions with societal benefit: Work in interdisciplinary teams on real-world challenges, explore emerging technologies, and develop innovative concepts that connect scientific advances with societal needs.

Contents

• System Thinking
• Deep Tech Exploration
• Interdisciplinary Team Collaboration
• Excursion CERN / Geneva
• Design Thinking and Innovation Methods (User Research, Tech Driven Opportunity Identification, Reframing, Prototyping, Testing)
• Methods for (digital) Fabrication in Makerspace
• Presentation and communication of solutions and concepts

Competencies

Students are able to

• Analyze new technologies in cooperation with researchers, as well as evaluate their potential
• Create Innovative application (while using deep tech) that are designed to be highly relevant to fulfill societal needs
• Bridge the gap between scientific inventions and societal needs by applying the design thinking process and using innovation methods
• Work effectively and appreciatively in an interdisciplinary team
• Take responsibility for one’s own learning
• Taking responsibility for the project progress and taking actions on the own initiative

Literature

• E. Isaacs and M. Szymanski, "The value of rapid ethnography," in Advancing Ethnography in Corporate Environments, B. Jordan, Ed. Walnut Creek, CA, USA: Left Coast Press, 2013, pp. 92–107.
• H. Rosen, S. Ciesinski, Note on Creating a Viable Venture: A Global Perspective. Stanford, CA, USA: Stanford Graduate School of Business, 2018. [Online]. Available: https://hbsp.harvard.edu/product/E651-PDF-ENG
• M. L. Patten and M. Newhart, Understanding Research Methods: An Overview of the Essentials, 11th ed. New York, NY, USA: Routledge, 2023.
• F. Uebernickel, L. Jiang, W. Brenner, B. Pukall, T. Naef, and B. Schindlholzer, Design Thinking: The Handbook. Singapore: World Scientific, 2020.
• S. I. Manthey, TAS Manual: Guide to the Technology Application Selection Framework. Karlsruhe, Deutschland: Karlsruher Institut für Technologie (KIT), 2024. doi: 10.5445/IR/1000173061. [Online]. Available: https://publikationen.bibliothek.kit.edu/1000173061

Lecturer

• Prof. Kirstin Kohler
• Chutimon Espedal

Recommended Previous Knowledge 

• English language proficiency
• Interest in emerging technologies
• Willingness to work in interdisciplinary teams

Module Details

Semester	1/2
Lecture Language	German and English
Frequency	Every Term
Credit Points (ECTS)	10
Course Coordinator	Prof. Kirstin Kohler
Duration	1 Semester
Study Focus	Medical Data Science (MDS) Software Engineering (SE)
Course Achievement	None
Prerequisite for exam	None
Exam	Continuous Assessment (CA)

Weekly Hours (SWS) 

Lecture	2 SWS
Lab	2 SWS
Sum	4 SWS

Work Load 

Lecture	60 h
Project	200 h
Workshop	40 h
Sum	300 h