STUDY PROJECTS

WHAT STUDENTS THINK OF US

Eva

“Im Wintersemester 19/20 habe ich meine Bachelorarbeit am Lehrstuhl von Herrn Prof. Dr. Maaß verfasst und ein relevantes und spannendes Thema im Bereich der Datenökonomien zugewiesen bekommen. Es wurde darauf geachtet, dass das Thema sich mit den in meinem Studiengang erlernten Fähigkeiten deckt. Bei Unklarheiten konnte ich mich stets an meine Betreuerin wenden und wurde konstruktiv unterstützt.”

Sonercan

“Ich habe mich stets gut betreut gefühlt. Bei aufkommenden Fragen wurde mir zügig Unterstützung geboten.”

Matthias

“My bachelor’s thesis gave me a good overview of how I put what I learned in my studies into practice. My supervisor always helped me when I had questions regarding technical or content-related matters.”

Bachelor Student

Students can work on their study project with us at the Chair in Information and Service Systems (ISS)as a part of their Master’s and Bachelor’s studies. Smart Service Engineering (SSE) develops solutions for the systematic design and development of smart service systems at the interface between technical and business issues. The research focuses on conceptual modeling and systematic development of smart services, the application of semantic web technologies, and the development of technological smart service platforms.The project topics for the study projects will be taken from the below listed projects of DFKI, which makes it possible for the students to experience the real business atmosphere, and gain practical insights.

WHO IS SUPERVISING?

Professor and chair holder

Univ.-Prof. Dr.-Ing. Wolfgang Maaß

Senior Researcher

Dr.-Ing. Sabine Janzen

PhD candidatet

Nurten Öksüz

Research Assistant

Hannah Stein

Research Assistant

Maxx Richard Rahman

Research Assistant

Dusan Dokic

Professor and chair holder

Univ.-Prof. Dr.-Ing. Wolfgang Maaß

Field of Research:

Data-driven Decision Making

Service Management

Data Economy

Digital Transformation through Artificial Intelligence

Conceptual Modeling

Quantum Computing in Enterprises

Domains: Industry, Commerce, Health and Sports

Senior Researcher

Dr.-Ing. Sabine Janzen

Field of Research:

Service & AI Engineering

Intelligent user interfaces, esp. communication in conflict situations / crisis

Responsible AI

PhD candidatet

Nurten Öksüz

Field of Research:

Smart Retail Services

Biosignals

Healthcare Services

Research Assistant

Hannah Stein

Field of Research:

Data Valuation and Monetization

Data Quality in Companies and Data Ecosystems

Data Ecosystem Design

Research Assistant

Maxx Richard Rahman

Field of Research:

AI in Sports

Generative Models

ML in Doping Science

Quantum Machine Learning

Smart Dreaming with AI

Research Assistant

Dusan Dokic

Projects:

 

QUASIM: QC-Enhanced Service Ecosystem for Simulation in Manufacturing

Quantum computing (QC) is currently developing rapidly in research, and the manufacturing industry which is highly dependant on high quality standards, can utilize it to avoid errors in manufacturing. Simulations which are based on physical and material science models and systems of equations, which place considerable demands on the engineering knowledge in modeling and the resources for simulation calculation, can be used to derive optimized parameterizations of the machines. 

3S PROJECT: Detection of sample swapping in sports using AI technologies

Anti-doping analysis is a crucial measure to fight against cheating and doping activities in sports. In the suspicious case of sample swapping, the anti-doping organisation with testing authority confirms it by performing DNA analysis across multiple samples. In this project, we expand the research ambit of the collaboration to improve the ability to uncover sample swapping by developing a pattern recognition/classification algorithm that provides a score of similarity of one sample steroid profile with all others provided by the same athlete. 

SPAICER: Scalable adaptive production systems through AI-based resilience optimization

In SPAICER, AI technologies are transformed into Smart Resilience Services (SRS) with a clear value proposition, integrated into production environments and networked with each other. To ensure the reusability of SRS and the exchange with partners (SRS ecosystems), platforms are developed and operated according to different “Industrie 4.0” standards on existing base platforms. To achieve this goal, (1) machine learning (ML) methods are particularly suitable for deriving forecasts and recommended actions from data, and (2) formal planning and inference (PI) methods.

 EVAREST: Generating and utilizing data products in the food industry through smart services

The aim of the project is to develop and use data products as an economic asset in the food production ecosystem based on an open, technical data platform that transcends company boundaries, as well as economic and legal utilization concepts.

ZUKIPRO Future Center for Human-Centered Artificial Intelligence (AI) in Production  

The ZuKIPro future center creates a practice-oriented format for consulting, qualification, testing and diffusion of digital technologies. It is intended to promote participatory work and technology design and enable SMEs to tap the potential of digital technologies in work and business processes. In addition, ZuKIPro is to act as a neutral contact and transfer point for SMEs, funding agencies, technology providers and social partners.

PAIRS: Privacy-Aware, intelligent and Resilient Crisis Management

The objective of the PAIRS project is to develop a service-oriented infrastructure that supports companies in implementing early warning systems for maintaining supply chains and productivity. At the same time, the availability of services is to be secured and marketability strengthened.