Description:

This page provides an overview of the Bachelor & Master Project (Wirtschaftsinformatik) and WIPRO topics offered by the Chair of Sustainability and Innovation in Digital Ecosystems (SUST). 

Incubate x Futures

This student project is open to students in the Faculty of Business Administration and Economics (core study program) and to students from other faculties via the E3 track. It is also open to master’s students in the Faculty of Computer Science (and E3). Teams of three to five students take first steps toward a digital venture addressing UN SDGs. You will frame a problem, research users and markets, design a business model (Teece, 2010), and build a digital MVP to validate core assumptions using lean experimentation (Blank, 2013). Mandatory participation in the Startup Olympics provides an intensive weekend with mentoring, workshops (business model & concept modelling, pitching, marketing), and structured feedback. Deliverables include an interim presentation, a functional MVP with documented iterations, a final presentation, and a written report. Successful teams can continue after the semester with Place Beyond Bytes (co-working, network, GPU/AI resources, workshops) to mature their venture. For more details, see the attached Incubate x Futures announcement.

Assessment: Startup Olympics participation + follow-up 5%, Mid-term evaluation 10%, Final presentation 35%, Written report (incl. MVP documentation) 50%.

Language: English (German helpful for the Startup Olympics).

Das Projekt steht Studierenden der Fakultät für Wirtschaftswissenschaften (Kernstudium) sowie anderen Fakultäten über E3 offen; ebenso Masterstudierenden der Fakultät für Informatik (und E3). Teams aus drei bis fünf Studierenden entwickeln eine digitale Venture-Idee zu UN-SDGs, erarbeiten ein Geschäftsmodell (Teece, 2010) und bauen ein digitales MVP, um Kernannahmen mittels Lean-Experimenten zu validieren (Blank, 2013). Die Teilnahme an den Startup Olympics ist verpflichtend und bietet ein intensives Wochenende mit Mentoring, Workshops (Businessplan & Konzeptmodellierung, Pitching, Marketing) und strukturiertem Feedback. Deliverables: Zwischenpräsentation, funktionsfähiges MVP mit dokumentierten Iterationen, Abschlusspräsentation und schriftlicher Bericht. Erfolgreiche Teams können anschließend mit Place Beyond Bytes (Co-Working, Netzwerk, GPU/AI-Ressourcen, Workshops) weiterarbeiten. Details siehe die Incubate-x-Futures-Ankündigung.

Bewertung: Startup-Olympics + Follow-up 5 %, Mid-Term 10 %, Abschlusspräsentation 35 %, Schriftlicher Bericht (inkl. MVP-Dokumentation) 50 %.

Sprache: Englisch (Deutsch für die Startup Olympics hilfreich).

Mandatory deliverables (minimum)

• Problem dossier
• Pitch deck (≤10–12 slides)
• Digital MVP (web/mobile/app/API/automation/agent) + repo & README
• Final business model report (incl. assumptions, experiments, metrics, ethics/data/privacy)

Participation & constraints

• Startup Olympics mandatory (attendance required to pass).
• MVP must be digital (no pure hardware-only projects).
• Team size: 3–5 recommended.
• Working language: English (Startup Olympics: German recommended).

Chronological schedule (Europe/Berlin)

• Kickoff – Incubate X Futures: Mon, 20 Oct 2025, 15:00–17:00 (CEST, UTC+2)
• BMC & Value Proposition: Mon, 27 Oct 2025, 15:00–17:00 (CET, UTC+1)
• Startup Olympics (MANDATORY): Fri–Sun, 7–9 Nov 2025 (CET, UTC+1)
• Business Plan: Mon, 17 Nov 2025, 15:00–17:00 (CET, UTC+1)
• MVP Workshop: Mon, 24 Nov 2025, 15:00–17:00 (CET, UTC+1)
• Midterm Presentation + MVP Status: Mon, 12 Jan 2026, 15:00–17:00 (CET, UTC+1)
• Final Presentation + MVP Showcase: Mon, 26 Jan 2026, 15:00–17:00 (CET, UTC+1), Location: PBB

WIPRO 

SUST-1-1 | Building a Multi-Source Dataset for Comparing Official and Unofficial Air-Raid Information in Ukraine

Supervisor: Albina Komysheva
Contact: Albina.Komysheva@ris.uni-due.de
Target group: Master's students
Language: English

This project investigates how information about aerial threats and attacks in Ukraine is communicated through different digital channels. Students will collect and integrate data from official alert systems, Telegram channels, news reports, and social media to compare the timing, geographic coverage, and content of warnings.

The project involves developing automated data-collection pipelines, creating a multi-source dataset, conducting comparative analyses, and building an interactive dashboard to visualize information flows.

Key areas: Data engineering, Python, web scraping, data integration, social media analytics, and data visualization.

SUST-1-2 | Deep Tech Decision Navigator: Building and Evaluating a Digital Tool for Evidence-Based Venture Strategy

Supervisor: Enzo Watermann
Contact: Enzo.watermann@bryck.com 
Target group: Bachelor's and Master's students 
Language: German or English 

Deep-tech startups face significant uncertainty when making decisions about technology development, market opportunities, and resource allocation.

In this project, students will design and develop a web-based decision-support tool that helps startup teams document assumptions, evidence, strategic decisions, and outcomes. The tool will support founders in reflecting on their decisions and understanding how their venture strategy evolves over time.

The project includes requirements analysis, prototype development, and evaluation with potential users or realistic venture cases.

SUST-1-3 | Can a Language Model Assess Funding Proposals? Building an Evaluation Benchmark for an AI Screening System (EXIST)

Supervisor: Elias Jelinek
Contact: elias.jelinek@uni-due.de
Target group: Bachelor's and Master's students
Language: German or English (German reading skills required)

This project explores whether large language models can reliably evaluate startup funding proposals, particularly applications to the German EXIST funding programmes.

Students will work with an existing AI-based screening system and develop a systematic evaluation benchmark to assess the quality and reliability of its recommendations. They will compare AI-generated assessments with known application outcomes, identify weaknesses, and implement and evaluate improvements.

SUST-1-4 | Grounding an AI Pitch Deck Evaluator: A Literature- and Evidence-Based Context Harness for the Pitch Deck Checker

Supervisor: Arslan Haider
Contact: arslan.haider@uni-due.de
Target group: Bachelor's and Master's students
Language: English

This project focuses on improving the reliability and quality of AI-generated feedback on startup pitch decks.

Students will extend an existing AI Pitch Deck Checker by developing an evidence-based knowledge base containing evaluation criteria, academic research, practitioner guidelines, and examples of real startup pitch decks.

The project involves integrating this knowledge into the AI evaluation system and testing whether the resulting feedback becomes more specific, consistent, and aligned with expert assessments.

Material:

• SS23_Project_IncubateXFutures.pdf (203 KB, Last change 5th Apr 2023)